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PubMed2005

The impact of computing technology on pharmaceutical and biotech research.

Pharmaceutical and biotechnology companies continue to make investments in research techniques including those using high-performance computing technology. Much of this research is fueled by the need to analyze the ever-growing store of genomic and proteomic data. However, many research and development investments, including techniques to mine newly produced genomic data, have not provided the anticipated improvements in productivity. This raises the questions of whether advancements expected from next-generation computing technology can translate into tangible benefits for pharmaceutical and biotechnology researchers, and whether researchers can capitalize on an increased understanding of biological systems using tools made more readily available in high-performance computing. The author provides some background on the source of the advancements in the computing industry and offers examples from other scientific applications that point to potential benefits in life science research.

Biotechnology↗

PubMed1997

Comparing the predicted and observed properties of proteins encoded in the genome of Escherichia coli K-12.

Mining the emerging abundance of microbial genome sequences for hypotheses is an exciting prospect of "functional genomics". At the forefront of this effort, we compared the predictions of the complete Escherichia coli genomic sequence with the observed gene products by assessing 381 proteins for their mature N-termini, in vivo abundances, isoelectric points, molecular masses, and cellular locations. Two-dimensional gel electrophoresis (2-DE) and Edman sequencing were combined to sequence Coomassie-stained 2-DE spots representing the abundant proteins of wild-type E. coli K-12 strains. Greater than 90% of the abundant proteins in the E. coli proteome lie in a small isoelectric point and molecular mass window of 4-7 and 10-100 kDa, respectively. We identified several highly abundant proteins, YjbJ, YjbP, YggX, HdeA, and AhpC, which would not have been predicted from the genomic sequence alone. Of the 223 uniquely identified loci, 60% of the encoded proteins are proteolytically processed. As previously reported, the initiator methionine was efficiently cleaved when the penultimate amino acid was serine or alanine. In contrast, when the penultimate amino acid was threonine, glycine, or proline, cleavage was variable, and valine did not signal cleavage. Although signal peptide cleavage sites tended to follow predicted rules, the length of the putative signal sequence was occassionally greater than the consensus. For proteins predicted to be in the cytoplasm or inner membrane, the N-terminal amino acids were highly constrained compared to proteins localized to the periplasm or outer membrane. Although cytoplasmic proteins follow the N-end rule for protein stability, proteins in the periplasm or outer membrane do not follow this rule; several have N-terminal amino acids predicted to destabilize the proteins. Surprisingly, 18% of the identified 2-DE spots represent isoforms in which protein products of the same gene have different observed pI and M(r), suggesting they are post-translationally processed. Although most of the predicted and observed values for isoelectric point and molecular mass show reasonable concordance, for several proteins the observed values significantly deviate from the expected values. Such discrepancies may represent either highly processed proteins or misinterpretations of the genomic sequence. Our data suggest that AhpC, CspC, and HdeA exist as covalent homomultimers, and that IcdA exists as at least three isoforms even under conditions in which covalent modification is not predicted. We enriched for proteins based on subcellular location and found several proteins in unexpected subcellular locations.

Amino Acid Sequence↗

PubMed2005

High throughput functional genomics: identification of novel genes with tumor suppressor phenotypes.

We have used a combination of high throughput functional genomics, computerized database mining and expression analyses to discover novel human tumor suppressor genes (TSGs). A genome-wide high throughput cDNA phenotype screen was established to identify genes that induce apoptosis or reduce cell viability. TSGs are expressed in normal tissue and frequently act by reduction of growth of transformed cells or induce apoptosis. In agreement with that and thus serving as platform validation, our pro-apoptotic hits included genes for which tumor suppressing activities were known, such as kangai1 and CD81 antigen. Additional genes that so far have been claimed as putative TSGs or associated with tumor inhibitory activities (prostate differentiation factor, hRAS-like suppressor 3, DPH2L1-like and the metastasis inhibitor Kiss1) were confirmed in their proposed TSG-like phenotype by functionally defining their growth inhibitory or pro-apoptotic function towards cancer cells. Finally, novel genes were identified for which neither association with cell growth nor with apoptosis were previously described. A subset of these genes show characteristics of TSGs because they (i) reduce the growth or induce apoptosis in tumor cells; (ii) show reduced expression in tumor vs. normal tissue; and (iii) are located on chromosomal (LOH-) loci for which cancer-associated deletions are described. The pro-apoptotic phenotype and differential expression of these genes in normal and malignant tissue make them promising target candidates for the diagnosis and therapy of various tumors.

Apoptosis↗

PubMed2004

Gene ontology application to genomic functional annotation, statistical analysis and knowledge mining.

While a massive amount of biomolecular information is increasingly accumulating in different databanks, on the other hand high-throughput technologies are generating a great quantity of data that need to be annotated with the genomic information available, and interpreted. To this aim, the use of specific ontologies can greatly help either in integrating different information stored within heterogeneous databanks, or in identifying and clustering sequence data sharing common characteristics. In the molecular biology domain, the Gene Ontology (GO) is the most developed and widely used ontology. To demonstrate its great utility in the annotation and biological interpretation of gene sets obtained by means of high-throughput experiments, we implemented the web application here described. It enables functional annotations of a given gene set on a genomic scale and across different species. Within our application the annotations provided by the GO vocabulary allow either to easily bind several information from different resources, or to cluster annotated genes according to their biological characteristics. Through the GO structure it is also possible to represent biological concepts with different specificity levels, from very general to very precise concepts. Furthermore, the statistical evaluation of the categorizations provided by the GO annotations enables to highlight the most significant biological characteristics of a gene set, and therefore to mine knowledge from data. Our created tool meets the need to manage a vast quantity of biological data with a simple user interface adapt also for users with limited informatics knowledge, leading them to evaluate the functional significance of experiment's results with graphical views and statistical indexes in a well-known web browser user interface.

Genomics↗

PubMed2004

Identification of ligands for two human bitter T2R receptors.

Earlier, a family of G protein-coupled receptors, termed T2Rs, was identified in the rodent and human genomes through data mining. It was suggested that these receptors mediate bitter taste perception. Analysis of the human genome revealed that the hT2R family is composed of 25 members. However, bitter ligands have been identified for only three human receptors so far. Here we report identification of two novel ligand-receptor pairs. hT2R61 is activated by 6-nitrosaccharin, a bitter derivative of saccharin. hT2R44 is activated by denatonium and 6-nitrosaccharin. Activation profiles for these receptors correlate with psychophysical data determined for the bitter compounds in human studies. Functional analysis of hT2R chimeras allowed us to identify residues in extracellular loops critical for receptor activation by ligands. The discovery of two novel bitter ligand-receptor pairs provides additional support for the hypothesis that hT2Rs mediate a bitter taste response in humans.

Amino Acid Sequence↗

PubMed2006

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

The goal of this chapter is to introduce some of the available computational methods for expression analysis. Genomic and proteomic experimental techniques are briefly discussed to help the reader understand these methods and results better in context with the biological significance. Furthermore, a case study is presented that will illustrate the use of these analytical methods to extract significant biomarkers from high-throughput microarray data. Genomic and proteomic data analysis is essential for understanding the underlying factors that are involved in human disease. Currently, such experimental data are generally obtained by high-throughput microarray or mass spectrometry technologies among others. The sheer amount of raw data obtained using these methods warrants specialized computational methods for data analysis. Biomarker discovery for neurological diagnosis and prognosis is one such example. By extracting significant genomic and proteomic biomarkers in controlled experiments, we come closer to understanding how biological mechanisms contribute to neural degenerative diseases such as Alzheimers' and how drug treatments interact with the nervous system. In the biomarker discovery process, there are several computational methods that must be carefully considered to accurately analyze genomic or proteomic data. These methods include quality control, clustering, classification, feature ranking, and validation. Data quality control and normalization methods reduce technical variability and ensure that discovered biomarkers are statistically significant. Preprocessing steps must be carefully selected since they may adversely affect the results of the following expression analysis steps, which generally fall into two categories: unsupervised and supervised. Unsupervised or clustering methods can be used to group similar genomic or proteomic profiles and therefore can elucidate relationships within sample groups. These methods can also assign biomarkers to sub-groups based on their expression profiles across patient samples. Although clustering is useful for exploratory analysis, it is limited due to its inability to incorporate expert knowledge. On the other hand, classification and feature ranking are supervised, knowledge-based machine learning methods that estimate the distribution of biological expression data and, in doing so, can extract important information about these experiments. Classification is closely coupled with feature ranking, which is essentially a data reduction method that uses classification error estimation or other statistical tests to score features. Biomarkers can subsequently be extracted by eliminating insignificantly ranked features. These analytical methods may be equally applied to genetic and proteomic data. However, because of both biological differences between the data sources and technical differences between the experimental methods used to obtain these data, it is important to have a firm understanding of the data sources and experimental methods. At the same time, regardless of the data quality, it is inevitable that some discovered biomarkers are false positives. Thus, it is important to validate discovered biomarkers. The validation process may be slow; yet, the overall biomarker discovery process is significantly accelerated due to initial feature ranking and data reduction steps. Information obtained from the validation process may also be used to refine data analysis procedures for future iteration. Biomarker validation may be performed in a number of ways - bench-side in traditional labs, web-based electronic resources such as gene ontology and literature databases, and clinical trials.

Animals↗

PubMed2006

Brain evolution and uniqueness in the human genome.

Despite an ever-expanding database of sequenced mammalian genomes to be mined for clues, the emergence of the unique human brain remains an evolutionary enigma. In their new study, trawl the human genome and those of other mammals in search of short conserved DNA elements that show extremely rapid evolution only in humans. As they report in a recent issue of Nature, their scan yielded a gene for a novel noncoding RNA that adopts a human-specific structure and may regulate neurodevelopment.

Animals↗

PubMed2026

pSTRminer: integrated bioinformatic software for genome-wide identification and population-scale evaluation of polymorphic short tandem repeats.

Animal forensic genetics plays a critical role in criminal investigations by providing crucial evidence through domestic animal individualization and wildlife species identification. While human forensic genetics benefits from standardized short tandem repeats (STR) genotyping systems, animal forensic applications encounter significant challenges, including the limited availability of validated STR markers, the prevalence of error-prone dinucleotide STRs (di-STRs), and insufficient integration of population data. To address these challenges, we developed pSTRminer, an integrated bioinformatic tool that automates genome-wide STR mining and polymorphism evaluation. By applying pSTRminer to domestic cattle (Bos taurus), we identified 775,444 STRs de novo from the reference genome and genotyped them using whole-genome sequencing data from 60 Chinese and 111 African cattle to evaluate polymorphism across diverse genetic backgrounds. This led to the development of the cattle STR database (CSDB), comprising loci with a genotyping success rate ≥ 40% and polymorphism information content (PIC) ≥ 0.5. Experimental validation of 30 randomly selected tetranucleotide STRs (tetra-STRs) and 33 di-STRs via next-generation sequencing in a local Chinese cattle population (n = 145) confirmed marker reliability. Although tetra-STRs had lower average polymorphism levels, they exhibited significantly lower stutter ratios (p < 0.05), providing a viable path for identifying discriminative markers with fewer artifacts. Systematic screening revealed that certain tetra-STRs could surpass di-STRs in polymorphism. In conclusion, pSTRminer provides a scalable framework for developing standardized STR panels, facilitating the identification of robust and informative markers in forensic applications.

Bioinformatic software↗

PubMed2003

The TIGR rice genome annotation resource: annotating the rice genome and creating resources for plant biologists.

Rice is not only a major food staple for the world's population but it also is a model species for a major group of flowering plants, the monocotyledonous plants. Draft genomic sequence of two subspecies of rice, Oryza sativa spp. japonica and indica ssp. are publicly available. To provide the community with a resource to data-mine the rice genome, we have constructed an annotation resource for rice (http://www.tigr.org/tdb/e2k1/osa1/). In this resource, we have annotated the rice genome for gene content, identified motifs/domains within the predicted genes, constructed a rice repeat database, identified related sequences in other plant species, and identified syntenic sequences between rice and maize. All of the data is available through web-based interfaces, FTP downloads, and a Distributed Annotation System.

Chromosomes, Artificial↗

PubMed1999

Recently integrated human Alu repeats: finding needles in the haystack.

Alu elements undergo amplification through retroposition and integration into new locations throughout primate genomes. Over 500,000 Alu elements reside in the human genome, making the identification of newly inserted Alu repeats the genomic equivalent of finding needles in the haystack. Here, we present two complementary methods for rapid detection of newly integrated Alu elements. In the first approach we employ computational biology to mine the human genomic DNA sequence databases in order to identify recently integrated Alu elements. The second method is based on an anchor-PCR technique which we term Allele-Specific Alu PCR (ASAP). In this approach, Alu elements are selectively amplified from anchored DNA generating a display or 'fingerprint' of recently integrated Alu elements. Alu insertion polymorphisms are then detected by comparison of the DNA fingerprints generated from different samples. Here, we explore the utility of these methods by applying them to the identification of members of the smallest previously identified subfamily of Alu repeats in the human genome termed Ya8. This subfamily of Alu repeats is composed of about 50 elements within the human genome. Approximately 50% of the Ya8 Alu family members have inserted in the human genome so recently that they are polymorphic, making them useful markers for the study of human evolution.

Animals↗

PubMed2001

The human genome, implications for oral health and diseases, and dental education.

We are living in an extraordinary time in human history punctuated by the convergence of major scientific and technological progress in the physical, chemical, and biological ways of knowing. Equally extraordinary are the sparkling intellectual developments at the interface between fields of study. One major example of an emerging influence on the future of oral health education is at the interface between the human genome, information technology, and biotechnology with miniaturizations (nanotechnology), suggesting new oral health professional competencies for a new century. A great deal has recently been learned from human and non-human genomics. Genome databases are being "mined" to prompt hypothesis-driven "postgenomic" or functional genomic science in microbial models such as Candida albicans related to oral candidiasis and in human genomics related to biological processes found in craniofacial, oral, and dental diseases and disorders. This growing body of knowledge is already providing the gene content of many oral microbial and human genomes and the knowledge of genetic variants or polymorphisms related to disease, disease progression, and disease response to therapeutics (pharmacogenomics). The knowledge base from human and non-human genomics, functional genomics, biotechnology, and associated information technologies is serving to revolutionize oral health promotion, risk assessment using biomarkers and disease prevention, diagnostics, treatments, and the full range of therapeutics for craniofacial, oral, and dental diseases and disorders. Education, training, and research opportunities are already transforming the curriculum and pedagogy for undergraduate science majors, predoctoral health professional programs, residency and specialty programs, and graduate programs within the health professions. In the words of Bob Dylan, "the times they are a-changing."

Biotechnology↗

PubMedDate not supplied

Analysis of genomic and proteomic data using advanced literature mining.

High-throughput technologies, such as proteomic screening and DNA micro-arrays, produce vast amounts of data requiring comprehensive analytical methods to decipher the biologically relevant results. One approach would be to manually search the biomedical literature; however, this would be an arduous task. We developed an automated literature-mining tool, termed MedGene, which comprehensively summarizes and estimates the relative strengths of all human gene-disease relationships in Medline. Using MedGene, we analyzed a novel micro-array expression dataset comparing breast cancer and normal breast tissue in the context of existing knowledge. We found no correlation between the strength of the literature association and the magnitude of the difference in expression level when considering changes as high as 5-fold; however, a significant correlation was observed (r = 0.41; p = 0.05) among genes showing an expression difference of 10-fold or more. Interestingly, this only held true for estrogen receptor (ER) positive tumors, not ER negative. MedGene identified a set of relatively understudied, yet highly expressed genes in ER negative tumors worthy of further examination.

Abstracting and Indexing↗

PubMed2001

The human olfactory subgenome: from sequence to structure and evolution.

Olfactory receptors (ORs) constitute the largest multigene family in multicellular organisms. Their evolutionary proliferation has been driven by the need to provide recognition capacity for millions of potential odorants with arbitrary chemical configurations. Human genome sequencing has provided a highly informative picture of the "olfactory subgenome", the repertoire of OR genes. We describe here an analysis of 224 human OR genes, a much larger number than hitherto systematically analyzed. These are derived by literature survey, data mining at 14 genomic clusters, and by an OR-targeted experimental sequencing strategy. The presented set contains at least 53% pseudogenes and is minimally divided into 11 gene families. One of these (no. 7) has undergone a particularly extensive expansion in primates. The analysis of this collection leads to insight into the origin of OR genes, suggesting a graded expansion through mammalian evolution. It also allows us to delineate a structural map of the respective proteins. A sequence database and analysis package is provided (http://bioinformatics.weizmann.ac.il/HORDE), which will be useful for analyzing human OR sequences genome-wide.

Amino Acid Sequence↗

PubMed2005

Integrating gene and protein expression data: pattern analysis and profile mining.

Proteomics and functional genomics are emerging new research fields devoted to the study of the entire collection of proteins and mRNA transcripts (collectively known as gene products) that define a biological system. DNA microarrays are now a popular platform for measuring changes in messenger RNA transcript levels on a genome-wide scale, while gel-free shotgun profiling methods based on tandem mass spectrometry are increasingly being used to determine the identity, modification states, and relative abundance of large numbers of proteins. By defining the behavior of entire biological pathways and networks under various physiological states, these studies aim to extend traditional reductionist molecular genetic approaches regarding the biological roles of the vast array of uncharacterized gene products. A key goal is to determine how the information encoded by the myriad of expressed gene products is integrated at the molecular, cellular, and even whole organism level to create the dynamic biochemical processes and complex physiological controls that sustain life. While comparison of the complementary information contained in proteomic and mRNA data sets poses considerable analytical challenges, these efforts should provide added insight into the fundamental mechanisms underlying physiology, development, and the emergence of disease. Here, we outline several analytical approaches, methods, and tools that have proven to be helpful in the face of this important challenge.

Algorithms↗

PubMed2003

Data mining the p53 pathway in the Fugu genome: evidence for strong conservation of the apoptotic pathway.

The p53 tumour suppressor gene belongs to a small family of related proteins that includes two other members, p63 and p73. Phylogenetic and functional studies suggest that p63 and p73 are ancient genes that have essential roles in normal development, whereas p53 seems to have evolved more recently to prevent cell transformation. In mammalian cells, a plethora of proteins have been found to specifically regulate p53 activity. The genome of the fish Fugu rubripes has been recently published. It is the second vertebrate genome for which the entire sequence is now available. Phylogenetic studies are essential in order to analyse and define signalling pathways important for cell cycle regulation. The presence or absence of a critical member in any pathway can shed light about the evolution of these pathways. The Fugu genome databank has been analysed for several members of the p53 network, including p53, p63 and p73. A good conservation of the network that regulates p53 stability and apoptosis has been found. We also discovered that some cofactors that cooperate with p53 for apoptosis are also well conserved and belong to multigene families not detected in the human genome.

Animals↗

PubMed2005

Genomic analysis of PIS1 gene expression.

The Saccharomyces cerevisiae PIS1 gene is essential and required for the final step in the de novo synthesis of phosphatidylinositol. Transcription of the PIS1 gene is uncoupled from the factors that regulate other yeast phospholipid biosynthetic genes. Most of the phospholipid biosynthetic genes are regulated in response to inositol and choline via a regulatory circuit that includes the Ino2p:Ino4p activator complex and the Opi1p repressor. PIS1 is regulated in response to carbon source and anaerobic growth conditions. Both of these regulatory responses are modest, which is not entirely surprising since PIS1 is essential. However, even modest regulation of PIS1 expression has been shown to affect phosphatidylinositol metabolism and to affect cell cycle progression. This prompted the present study, which employed a genomic screen, database mining, and more traditional promoter analysis to identify genes that affect PIS1 expression. A screen of the viable yeast deletion set identified 120 genes that affect expression of a PIS1-lacZ reporter. The gene set included several peroxisomal genes, silencing genes, and transcription factors. Factors suggested by database mining, such as Pho2 and Yfl044c, were also found to affect PIS1-lacZ expression. A PIS1 promoter deletion study identified an upstream regulatory sequence element that was required for carbon source regulation located downstream of three previously defined upstream activation sequence elements. Collectively, these studies demonstrate how a collection of genomic and traditional strategies can be implemented to identify a set of genes that affect the regulation of an essential gene.

Gene Expression Profiling↗

PubMed1998

[Compact genomes].

The number of procaryotic genomes (both Archaea and Bacteria) completely sequenced is rapidly increasing since the publication in 1995 of the first ever finished one, Haemophilis influenzae. The small size and "simplicity" of these genomes make them ideal models for training in genomic before attacking more complex genomes, but they have also great intrinsic interest. Preliminary analyses of these compact genomes have detected many orphan genes, even in organisms previously extensively studied, as well as many families of duplicated genes. A major task now is to identify the function of these orphans by a combination of in silico, biochemical and genetic analyses (examples will be presented). Several genomes of hyperthermophiles have been or will be completely sequenced soon. Many of their genes have commercial (stable proteins), as well as medical interest (crystallization of proteins with eucaryotic homologs involved in pathogenesis). However, further work with these genomes will require the development of genetic tools for these hyperthermophiles. The complete understanding of genome evolution, structure and function will require the sequencing of many genomes at the different levels of the evolutionary scale. Sequencing of genomes from closely related organisms can be relevant to study genome plasticity, whilst sequencing of genome from different domains (Archaea, Bacteria, Eucarya) can help to reconstruct the Last Universal Common Ancestor (LUCA). The latter is a difficult task and will require not only classical molecular phylogenetic studies (which can be sometimes greatly misleading) but also in depth comparative analyses of all central genetic mechanisms in the three domains to infer their respective evolution. The fundamental problem is to determine if the compact genome of procaryotes is indeed a primitive one (as suggested by the term procaryote itself) or if it has been compacted from a more complex one by evolutionary forces related to the procaryotic way of life. Finally, taking into account the extreme diversity of procaryotes and their metabolism, it should be kept in mind that beside a core of genes essential for cellular life, the myriad of procaryotic genomes contain a mine of non essential genes with potential commercial or medical application. The total number of these genes probably outnumber the total number of eucaryotic genes.

Evolution, Molecular↗

PubMed2006

Rnall: an efficient algorithm for predicting RNA local secondary structural landscape in genomes.

BACKGROUND: The information of RNA local secondary structures (LSSs) can help retrieve biologically important motifs and study functions of RNA molecules. Most of the current RNA secondary structure prediction tools are not suitable for RNA LSS prediction on the genome scale due to high computational complexity. METHODS: We developed a new computer package Rnall based on a dynamic programming technique, which scans an RNA sequence with a sliding window and extracts all RNA LSSs with sizes no larger than the window size using the nearest neighbor thermodynamic parameters. The worst case running time of Rnall is O(W(3)L), where W is the window size and L is the query sequence length. In practice we observed a running time of O(W(2)L). We further introduced the concept of energy landscape for illustrating RNA LSS, which may facilitate RNA motif mining on the genomic scale. RESULTS: Rnall shows better prediction accuracy than two other prediction tools Lfold and Quickfold. Rnall is also applied to scan for RNA LSSs in three genomes, and the prediction maps well with known RNA motifs. CONCLUSIONS: Rnall is designed for RNA LSS prediction and together with the energy landscape, it has unique features that could be used for RNA structural motif mining. Rnall is freely available for download at http://digbio.missouri.edu/~wanx/Rnall or http://www.sysbio.muohio.edu/Rnall.

Algorithms↗