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The current research considers the approximate string matching search for important subsequences from DNA sequences, which is essential for numerous bioinformatics computation tasks. We tested several approximate string matching algorithms and furthermore developed one for DNA data. Run times of the algorithms are important, since the amount of data is very large.
DNA microarrays may be used to identify potential molecular targets for drug discovery. Yet, DNA microarray experiments provide massive amounts of data. To limit the choice of potential molecular targets, it may be desirable to eliminate genes coincidentally up-regulated in tissues implicated in absorption, distribution, metabolism, and excretion (ADME) pharmacokinetics. DNA microarray experiments were performed to demonstrate a gene-exclusion approach using as an example RNA samples of neural origin, i.e., a human neuroblastoma cell line (SK-N-SH) and brain tissue, as the intended hypothetical site(s) of drug action. Biomarkers were identified using PharmArray DNA microarrays. The lists of neuroblastoma and neural biomarkers were constrained by limiting selection to the subset of genes that were not highly expressed in three transformed cell lines from liver, colon, and kidney (HepG2, Caco-2, and 786-O, respectively) that are routinely used as representatives of the ADME system during in vitro pharmacology and toxicology experiments. Principal component analysis methods with likelihood ratio-related bioinformatic tools were utilized to identify robust potential biomarker genes for the three ADME-related cell lines, neuroblastoma, and normal brain. Biomarkers of each sample were identified and selected genes were validated by qRT-PCR. Hundreds of biomarkers of the three ADME-related cell types, representing hepatocytes, kidney epithelium, and gastrointestinal tract, may now be used as a valuable database to restrict selection of biomarkers as potential molecular targets from the intended samples (e.g., neuroblastoma in this work). In addition to biomarker discovery per se, this demonstration suggests that our model method may be viable to help restrict gene lists during selection of potential molecular targets for subsequent drug discovery.
We have created a molecular resource of genes expressed in primary malignant plasma cells using a combination of cDNA library construction, 5' end single-pass sequencing, bioinformatics, and microarray analysis. In total, we identified 9732 nonredundant expressed genes. This dataset is available as the Myeloma Gene Index (www.uhnres.utoronto.ca/akstewart_lab).Predictably, the sequenced profile of myeloma cDNAs mirrored the known function of immunoglobulin-producing, high-respiratory rate, low-cycling, terminally differentiated plasma cells. Nevertheless, approximately 10% of myeloma-expressed sequences matched only entries in the database of Expressed Sequence Tags (dbEST) or the high-throughput genomic sequence (htgs) database. Numerous novel genes of potential biologic significance were identified. We therefore spotted 4300 sequenced cDNAs on glass slides creating a myeloma-enriched microarray. Several of the most highly expressed genes identified by sequencing, such as a novel putative disulfide isomerase (MGC3178), tumor rejection antigen TRA1, heat shock 70-kDa protein 5, and annexin A2, were also differentially expressed between myeloma and B lymphoma cell lines using this myeloma-enriched microarray. Furthermore, a defined subset of 34 up-regulated and 18 down-regulated genes on the array were able to differentiate myeloma from nonmyeloma cell lines. These not only include genes involved in B-cell biology such as syndecan, BCMA, PIM2, MUM1/IRF4, and XBP1, but also novel uncharacterized genes matching sequences only in the public databases. In summary, our expressed gene catalog and myeloma-enriched microarray contains numerous genes of unknown function and may complement other commercially available arrays in defining the molecular portrait of this hematopoietic malignancy. GenBank Accession numbers include BF169967-BF176369, BF185966-BF185969, and BF177280-BF177455.
INTRODUCTION: The enzyme telomerase regulates telomere length by synthesis of telomeric repeats to compensate for telomeric loss in each DNA replication cycle. Therefore, telomerase is a potential target to block growth of cells with high replication rates. In Plasmodium falciparum, telomerase activity has been documented, but little information on its structure and role. METHODS: Herein, alignment of multiple sequences was undertaken comparing telomerase catalytic subunit sequences as found in existing databases. A consensus sequence was compared with the sequences in the P. falciparum genome project and as a result, a candidate sequence for a portion of the telomerase gene was recovered. Primer sets were designed for DNA and RNA amplifications. RESULTS: DNA fragments corresponding to telomerase conserved domains were amplified by using reverse transcription and PCR of cDNA. With a combination of bioinformatics and sequencing methods, the sequence of telomerase catalytic subunit gene (TERT) in P. falciparum was discovered, and its presence and transcription demonstrated.
Many biological signaling pathways involve autocrine ligand-receptor loops; misregulation of these signaling loops can contribute to cancer phenotypes. Here we present an algorithm for detecting such loops from gene expression profiles. Our method is based on the hypothesis that for some autocrine pathways, the ligand and receptor are regulated by coupled mechanisms at the level of transcription, and thus ligand-receptor pairs comprising such a loop should have correlated mRNA expression. Using our database of experimentally known ligand-receptor signaling partners, we found examples of ligand-receptor pairs with significantly correlated expression in five cancer-based gene expression datasets. The correlated ligand-receptor pairs we identified are consistent with known autocrine signaling events in cancer cells. In addition, our algorithm predicts new autocrine signaling loops that can be verified experimentally. Chemokines were commonly members of these potential autocrine pathways. Our analysis also revealed ligand-receptor pairs with expression patterns that may indicate cellular mechanisms for preventing autocrine signaling.
Protein phosphorylation is important for regulation of most biological functions and up to 50% of all proteins are thought to be modified by protein kinases. Increased knowledge about potential phosphorylation of a protein may increase our understanding of the molecular processes in which it takes part. Despite the importance of protein phosphorylation, identification of phosphoproteins and localization of phosphorylation sites is still a major challenge in proteomics. However, high-throughput methods for identification of phosphoproteins are being developed, in particular within the fields of bioinformatics and mass spectrometry. In this review, we present a toolbox of current technology applied in phosphoproteomics including computational prediction, chemical approaches and mass spectrometry-based analysis, and propose an integrated strategy for experimental phosphoproteomics.
A reverse transcription-polymerase chain reaction (RT-PCR) approach was used to clone a cDNA encoding the Euglena gracilis homolog of yeast Cbf5p, a protein component of the box H/ACA class of snoRNPs that mediate pseudouridine formation in eukaryotic rRNA. Cbf5p is a putative pseudouridine synthase, and the Euglena homolog is the first full-length Cbf5p sequence to be reported for an early diverging unicellular eukaryote (protist). Phylogenetic analysis of putative pseudouridine synthase sequences confirms that archaebacterial and eukaryotic (including Euglena) Cbf5p proteins are specifically related and are distinct from the TruB/Pus4p clade that is responsible for formation of pseudouridine at position 55 in eubacterial (TruB) and eukaryotic (Pus4p) tRNAs. Using a bioinformatics approach, we also identified archaebacterial genes encoding candidate homologs of yeast Gar1p and Nop10p, two additional proteins known to be associated with eukaryotic box H/ACA snoRNPs. These observations raise the possibility that pseudouridine formation in archaebacterial rRNA may be dependent on analogs of the eukaryotic box H/ACA snoRNPs, whose evolutionary origin may therefore predate the split between Archaea (archaebacteria) and Eucarya (eukaryotes). Database searches further revealed, in archaebacterial and some eukaryotic genomes, two previously unrecognized groups of genes (here designated 'PsuX' and 'PsuY') distantly related to the Cbf5p/TruB gene family.
It was recently reported that congenital heart disease is significantly more frequent in patients with NF1 microdeletion syndrome than in those with classical NF1. The outcome of congenital heart disease in this subset of patients is likely caused by the haploinsufficiency of gene/s in the deletion interval. Following in silico analysis of the deleted region, we found two genes known to be expressed in adult heart, the Joined to JAZF1 (SUZ12) and the Centaurin-alpha 2 (CENTA2) genes, and seven other genes with poorly defined patterns of expression and function. With the aim of defining their expression profiles in human fetal tissues (15th-21st weeks of gestation), expression analysis by RT-PCR and Northern blotting was performed. C17orf40, SUZ12 and CENTA2 were found to be mainly expressed in fetal heart, and following RT-PCR on mouse embryos and embryonic heart and brain at different stages of development, we found that the orthologous genes C17orf40, Suz12 and Centa2 are also expressed in early stages of development, before and during the formation of the four heart chambers. The presence of binding sites for Nkx2-5, a transcription factor expressed early in heart development, in all three mouse orthologous genes was predicted by bioinformatics, thus reinforcing the hypothesis that these genes might be involved in heart development and may be plausible candidates for congenital heart disease.
BACKGROUND: Escherichia coli guanine-N2 (m2G) methyltransferases (MTases) RsmC and RsmD modify nucleosides G1207 and G966 of 16S rRNA. They possess a common MTase domain in the C-terminus and a variable region in the N-terminus. Their C-terminal domain is related to the YbiN family of hypothetical MTases, but nothing is known about the structure or function of the N-terminal domain. RESULTS: Using a combination of sequence database searches and fold recognition methods it has been demonstrated that the N-termini of RsmC and RsmD are related to each other and that they represent a "degenerated" version of the C-terminal MTase domain. Novel members of the YbiN family from Archaea and Eukaryota were also indentified. It is inferred that YbiN and both domains of RsmC and RsmD are closely related to a family of putative MTases from Gram-positive bacteria and Archaea, typified by the Mj0882 protein from M. jannaschii (1dus in PDB). Based on the results of sequence analysis and structure prediction, the residues involved in cofactor binding, target recognition and catalysis were identified, and the mechanism of the guanine-N2 methyltransfer reaction was proposed. CONCLUSIONS: Using the known Mj0882 structure, a comprehensive analysis of sequence-structure-function relationships in the family of genuine and putative m2G MTases was performed. The results provide novel insight into the mechanism of m2G methylation and will serve as a platform for experimental analysis of numerous uncharacterized N-MTases.
The aim of this chapter is to provide instruction for analyzing and mapping recent segmental and gene duplications in eukaryotic genomes. We describe a bioinformatics-based approach utilizing computational tools to manage eukaryotic genome sequences to characterize and understand the evolutionary fates and trajectories of duplicated genes. An introduction to bioinformatics tools and programs such as BLAST, Perl, BioPerl, and the GFF specification provides the necessary background to complete this analysis for any eukaryotic genome of interest.
BACKGROUND: Anesthetic drugs may alter exosomal microRNA (miRNA) contents and mediate cancer progression and tumor microenvironment remodeling. Our study aims to explore how the anesthetics (sevoflurane and propofol) impact the miRNA makeup within exosomes in hepatocellular carcinoma (HCC), alongside the interconnected signaling pathways linked to the tumor immune microenvironment. METHODS: In this prospective study, we collected plasma exosomes from two groups of HCC patients (n = 5 each) treated with either propofol or sevoflurane, both before anesthesia and after hepatectomy. Exosomal miRNA profiles were assessed using next-generation sequencing (NGS). Furthermore, the expression data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) was used to pinpoint the differentially expressed exosomal miRNAs (DEmiRNAs) attributed to the influence of propofol or sevoflurane in the context of HCC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to dissect the signaling pathways and biological activities associated with the identified DEmiRNAs and their corresponding target genes. RESULTS: A total of 35 distinct DEmiRNAs were exclusively regulated by either propofol (n = 9) or sevoflurane (n = 26). Through TCGA-LIHC database analysis, 8 DEmiRNAs were associated with HCC. These included propofol-triggered miR-452-5p and let-7c-5p, as well as sevoflurane-induced miR-24-1-5p, miR-122-5p, miR-200a-3p, miR-4686, miR-214-3p, and miR-511-5p. Analyses revealed that among these 8 DEmiRNAs, the upregulation of miR-24-1-5p consistently demonstrated a significant association with lower histological grades (p < 0.0001), early-stage tumors (p < 0.05) and higher survival (p = 0.029). Further analyses using GSEA and GSVA indicated that miR-24-1-5p, along with its target genes, were involved in governing the tumor immune microenvironment and potentially inhibiting tumor progression in HCC. CONCLUSIONS: This study provided bioinformatics evidence suggesting that sevoflurane-induced plasma exosomal miRNAs may have a potential impact on the immune microenvironment of HCC. These findings established a foundation for future research into mechanistic outcomes in cancer patients.
Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.
BACKGROUND: The most common malignant type of kidney cancer is clear cell renal cell carcinoma (ccRCC). The expression levels of hyaluronan-mediated motility receptor (HMMR) in many tumor types are significantly elevated. HMMR is closely associated with tumor-related progression, treatment resistance, and poor prognosis, and has yet to be fully investigated in terms of its expression patterns and molecular mechanisms of action in ccRCC. Further research is imperative to elucidate these aspects. METHODS: We used The Cancer Genome Atlas (TCGA) database to preliminarily investigate HMMR expression and function in ccRCC and the data for 19 samples from the NCBI GEO database (GSE207493) for single-cell analysis. We assessed the differential expression level of HMMR between ccRCC cancerous tissues and their matched non-tumor tissues. Subsequently, a series of in vivo and in vitro experiments were designed to elucidate the biological function of HMMR in ccRCC, including Transwell assays, CCK-8 assays, clone formation assays and subcutaneous xenograft experiments in nude mice. Through bioinformatics analysis, we identified potential microRNAs (miRNAs) that may regulate HMMR, as well as the possible signaling pathways involved. Finally, we conducted a series of cellular functional experiments to validate our hypotheses regarding the HMMR axis. RESULTS: HMMR expression was significantly up-regulated in tumor tissues of ccRCC patients, and elevated HMMR expression level showed a strong correlation with ccRCC progression and adverse prognoses of patients. Knocking down HMMR inhibited the proliferative and migratory abilities of ccRCC cells, while its overexpression amplified these oncogenic properties. In nude mice model, reduced HMMR expression inhibited ccRCC tumor proliferation in vivo. Furthermore, overexpression of an upstream transcriptional regulator, miR-9-5p, effectively downregulated HMMR expression and thus impeded ccRCC cells proliferation and migration. HMMR might influence ccRCC growth via the Epithelial-Mesenchymal Transition (EMT) pathway and the Janus Kinase 1/Signal Transducer and Activator of Transcription 1 (JAK1/STAT1) pathway. CONCLUSIONS: HMMR is overexpressed in ccRCC, and there is a significant link between high HMMR expression and tumor progression, as well as poor patient prognosis. Specifically, HMMR could be targeted and inhibited by miR-9-5p and might modulate the tumorigenesis and progression of ccRCC through both EMT and JAK1/STAT1 signaling pathway.
As more and more protein sequences are available, homolog identification becomes increasingly important for functional, structural, and evolutional studies of proteins. Many homologous proteins were separated a very long time ago in their evolutionary history and thus their sequences share low sequence identity. These remote homologs have become a research focus in bioinformatics over the past decade, and some significant advances have been achieved. In this paper, we provide a comprehensive review on computational techniques used in remote homolog identification based on different methods, including sequence-sequence comparison, and sequence-structure comparison, and structure-structure comparison. Other miscellaneous approaches are also summarized. Pointers to the online resources of these methods and their related databases are provided. Comparisons among different methods in terms of their technical approaches, their strengths, and limitations are followed. Studies on proteins in SARS-CoV are shown as an example for remote homolog identification application.
The data explosion in bioinformatics is relentless. More and more genomes are being sequenced and many new types of datasets are being generated in large-scale projects. Integration and true open access to the data are still difficult issues, although they are gradually being addressed. Notably, certain fields have good standardization and interoperability, while others lag behind. This review summarizes the latest developments in genome and sequences databases, transcriptomics data (ESTs, ORESTES, full-length cDNAs), proteomics data (protein databases, protein structures, family and domain classification) as well as loosely integrated fields, such as microarray experiments, mutation databases and databases of regulatory regions and elements. The review attempts to resist simply summarizing what data are available, and aims to provide a critical look at some of the integration and access issues associated with several of these resources.
PURPOSE: To identify olfactomedin domain containing proteins, which are expressed in the eye and have similarity to myocilin, to test as potential candidates for eye diseases. Most of the mutations in myocilin causing primary open angle glaucoma are located in the olfactomedin domain. In vitro experiments demonstrated interaction between optimedin and myocilin through the conserved olfactomedin domains of the proteins in rats, and it was speculated that optimedin might have a role in the pathogenesis of ocular disorders. Hence, we aimed to identify myocilin related human proteins having conserved olfactomedin domains with potential to interact between them and examine the expression patterns in the eye by bioinformatics approaches. This endeavor would have the potential to identify new candidate genes for eye diseases in general and glaucoma in particular to be tested by wet-lab experiments. METHODS: Proteins with homology to myocilin were selected by BLASTp at the NCBI server. cDNA sequences and corresponding genomic contigs were retrieved. Pairwise BLAST was done to investigate the gene structure. The human EST database and NEIBank were searched against the selected cDNAs to look for tissue specific expression of the transcripts. RESULTS: The study led to the identification of three groups of proteins encoded by three different genes; Noelin 1 (9q34.3), Noelin 2 (19p13.2), and Noelin 3 (1p22) encompassing 45,575 bp, 82,679 bp, and 1,93,421 bp of the genomic sequence, respectively. Genomic structures, alternate usage of exons, and molecular evolution of the Noelins were determined. Similar structures of the genes, splicing patterns and high levels of homology shed light on the relatedness and molecular evolution of this group of olfactomedin related proteins. Strikingly, however, Noelin 1 and Noelin 3 were found to be expressed as multiple splice variants while only a single spliced transcript could be identified for Noelin 2. A human EST database search suggested the expression of all three Noelin genes in the brain but only two (Noelin 1 and Noelin 2) in the eye despite experimental evidence for expression of Noelin 3 in ocular tissue. Myocilin was determined to have similar levels (60-61%) of homology with all three Noelin gene products (Noelin 1_v1, Noelin 2_v1, and Noelin 3_v1) at the conserved olfactomedin domains. CONCLUSIONS: Mammalian Noelin 1 evolved from its precursor, followed by evolution of Noelin 3 and Noelin 2 by gene duplication events. Myocilin might have evolved from Noelin 2 by gene duplication followed by exon fusion. Noelin 1 and Noelin 2 could be tested as candidate genes for eye diseases based on their expressions in the eye and shared olfactomedin domains with Myocilin in the C-termini of the respective proteins.
Phage display technology provides a versatile tool for exploring the interactions between proteins, peptides and small molecule ligands. Quantitative analysis of peptide population sequence diversity and bias patterns has the power to significantly enhance the impact of these methods [1, 2]. We have developed a suite of computational tools for the analysis of peptide populations and made them accessible by integrating fifteen software programs for the analysis of combinatorial peptide sequences into the REceptor LIgand Contacts (RELIC) relational database and web-server. These programs have been developed for the analysis of statistical properties of peptide populations; identification of weak consensus sequences within these populations; and the comparison of these peptide sequences to those of naturally occurring proteins. RELIC is particularly suited to the analysis of peptide populations affinity selected with a small molecule ligand such as a drug or metabolite. Within this functional context, the ability to identify potential small molecule binding proteins using combinatorial peptide screening will accelerate as more ligands are screened and more genome sequences become available. The broader impact of this work is the addition of a novel means of analyzing peptide populations to the phage display community.
We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.