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Proteomics: techniques and applications in cancer research.

Proteomics can be defined as functional analysis of the full set of proteins by high-throughput technologies in a given system. The workflow of proteomics is a multi-step process comprising sample preparation, separation, quantification and identification of proteins. Due to the high complexity of different protein species and the wide dynamic range of protein amount within a cell system it is necessary to apply appropriate analysis methods. One approach is to separate proteins first by two-dimensional gel electrophoresis (2-DE) according to charge and molecular weight. Proteins are then fragmented and analyzed using matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS). Identification of proteins can be achieved by comparing the mass/charge-ratios of these peptides to respective databases. Proteome analysis with respect to the identification of disease-associated patterns of molecules in different tissues is in the early stages, because standardisation of these techniques often remains to be established. However, proteome analyses is a promising tool to obtain holistic insights into the physiological status of a cell or cellular system. Compared to RNA-based studies some advantages are obvious: (1) post-translational modifications, e. g. phosphorylation, contributing to the activity status can be detected at the protein level only, (2) RNA-levels do not necessarily coincide with protein levels for a particular gene, (3) feedback-mechanisms within regulatory pathways can control protein activity without measurable changes in mRNA content.

Algorithms↗
PubMed2004

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data.

The dissection of complex biological systems is a challenging task, made difficult by the size of the underlying molecular network and the heterogeneous nature of the control mechanisms involved. Novel high-throughput techniques are generating massive data sets on various aspects of such systems. Here, we perform analysis of a highly diverse collection of genomewide data sets, including gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. By integrating experimental data of heterogeneous sources and types, we are able to perform analysis on a much broader scope than previous studies. At the core of our methodology is the ability to identify modules, namely, groups of genes with statistically significant correlated behavior across diverse data sources. Numerous biological processes are revealed through these modules, which also obey global hierarchical organization. We use the identified modules to study the yeast transcriptional network and predict the function of >800 uncharacterized genes. Our analysis framework, SAMBA (Statistical-Algorithmic Method for Bicluster Analysis), enables the processing of current and future sources of biological information and is readily extendable to experimental techniques and higher organisms.

Amino Acids↗
PubMed2003

Generation and evaluation of a large mutational library from the Escherichia coli mechanosensitive channel of large conductance, MscL: implications for channel gating and evolutionary design.

Random mutagenesis of the mechanosensitive channel of large conductance (MscL) from Escherichia coli coupled with a high-throughput functional screen has provided new insights into channel structure and function. Complementary interactions of conserved residues proposed in a computational model for gating have been evaluated, and important functional regions of the channel have been identified. Mutational analysis shows that the proposed S1 helix, despite having several highly conserved residues, can be heavily mutated without significantly altering channel function. The pattern of mutations that make MscL more difficult to gate suggests that MscL senses tension with residues located near the lipid headgroups of the bilayer. The range of phenotypical changes seen has implications for a proposed model for the evolutionary origin of mechanosensitive channels.

Amino Acid Sequence↗
PubMed2003

AKT activity determines sensitivity to mammalian target of rapamycin (mTOR) inhibitors by regulating cyclin D1 and c-myc expression.

Prior work demonstrates that AKT activity regulates sensitivity of cells to G(1) arrest induced by mammalian target of rapamycin (mTOR) inhibitors such as rapamycin and CCI-779. To investigate this, a novel high-throughput microarray polysome analysis was performed to identify genes whose mRNA translational efficiency was differentially affected following mTOR inhibition. The analysis also allowed the assessment of steady-state transcript levels. We identified two transcripts, cyclin D1 and c-myc, which exhibited differential expression in an AKT-dependent manner: High levels of activated AKT resulted in rapamycin-induced down-regulation of expression, whereas low levels resulted in up-regulation of expression. To ectopically express these proteins we exploited the finding that the p27(kip1) mRNA was efficiently translated in the face of mTOR inhibition irrespective of AKT activity. Thus, the p27(kip1) 5'-untranslated region was fused to the cyclin D1 and c-myc coding regions and these constructs were expressed in cells. In transfected cells, expression of cyclin D1 or c-myc was not decreased by rapamycin. Most importantly, this completely converted sensitive cells to a phenotype resistant to G(1) arrest. Furthermore, the AKT-dependent differential expression patterns of these two genes was also observed in a mouse xenograft model following in vivo treatment with CCI-779. These results identify two critical downstream molecular targets whose expression is regulated by AKT activity and whose down-regulation is required for rapamycin/CCI-779 sensitivity.

Cell Line↗
PubMed2001

Individual metabolism should guide agriculture toward foods for improved health and nutrition.

Genomics and bioinformatics have the vast potential to identify genes that cause disease by investigating whole-genome databases. Comparison of an individual's geno-type with a genomic database will allow the prescription of drugs to be tailored to an individual's genotype. This same bioinformatic approach, applied to the study of human metabolites, has the potential to identify and validate targets to improve personalized nutritional health and thus serve to define the added value for the next generation of foods and crops. Advances in high-throughput analytic chemistry and computing technologies make the creation of a vast database of metabolites possible for several subsets of metabolites, including lipids and organic acids. In creating integrative databases of metabolites for bioinformatic investigation, the current concept of measuring single biomarkers must be expanded to 3 dimensions to 1) include a highly comprehensive set of metabolite measurements (a profile) by multiparallel analyses, 2) measure the metabolic profile of individuals over time rather than simply in the fasted state, and 3) integrate these metabolic profiles with genomic, expression, and proteomic databases. Application of the knowledge of individual metabolism will revolutionize the ability of nutrition to deliver health benefits through food in the same way that knowledge of genomics will revolutionize individual treatment of dis-ease with pharmaceuticals.

Biomarkers↗
PubMed2005

Significant impact of protein dispensability on the instantaneous rate of protein evolution.

The neutral theory of molecular evolution predicts that important proteins evolve more slowly than unimportant ones. High-throughput gene-knockout experiments in model organisms have provided information on the dispensability, and therefore importance, of thousands of proteins in a genome. However, previous studies of the correlation between protein dispensability and evolutionary rate were equivocal, and it has been proposed that the observed correlation is due to the covariation with the level of gene expression or is limited to duplicate genes. We here analyzed the gene dispensability data of the yeast Saccharomyces cerevisiae and estimated protein evolutionary rates by comparing S. cerevisiae with nine species of varying degrees of divergence from S. cerevisiae. The correlation between gene dispensability and evolutionary rate, although low, is highly significant, even when the gene expression level is controlled for or when duplicate genes are excluded. Our results thus support the hypothesis of lower evolution rates for more important proteins, a widely used principle in the daily practice of molecular biology. When the evolutionary rate is estimated from closely related species, the ratio between the mean rate of nonessential proteins to that of essential proteins is 1.4. This ratio declines to 1.1 when the evolutionary rate is estimated from distantly related species, suggesting that the importance of a protein may change in evolution, so the dispensability data obtained from a model organism only predicts a short-term rate of protein evolution. A comparison of the fitness contributions of orthologous genes in yeast and nematode supports this conclusion.

Animals↗
PubMed2000

Suggestions for the nomenclature of human alleles: relevance to ecogenetics, pharmacogenetics and molecular epidemiology.

The current number of 9422 symbols for human gene names (http://www.gene.ucl.ac.uk/nomenclature/) is expected to increase 7- to 15-fold over the next 2 years. In and around each gene, a tremendous degree of single-nucleotide polymorphism (SNP) heterogeneity is now realized to exist. This review is intended to be visionary, to point out some of the enormously complex nomenclature issues that we face, and to offer some reasonable solutions to these issues. For example, I believe that a 'gene' should be defined as that region from the furthest 5'-ward enhancer to at least 150 bases downstream of the last exon. Just as established rules are critically important for the systematic naming of all new genes, standardized nomenclature rules for the naming of allelic variants are also desperately needed. The evolving consensus for naming the alleles of all human genes (ideally based on evolutionarily diverging haplotype patterns) is described herein. Because of the anticipated explosion in finding new genes and allelic variants due to high-throughput resequencing and DNA-chip technologies, this excess of new knowledge will undoubtedly overwhelm their publication by scientific journals alone. I suggest that the best approach to this staggering 'information overload' is to place the data on appropriate web sites--with numerous links between sites, and frequent updates of all information--so that colleagues in all fields of medical and genetic research can remain knowledgeable. Examples of successful web sites to date include those for the cytochrome P450 (CYP) genes and human CYP alleles, UDP glycosyltransferase (UGT) genes and human alleles, human N-acetylaminotransferase (NAT2, NAT1) alleles, and aldehyde dehydrogenase (ALDH) genes and human alleles. Many more web sites will be necessary. For each site, the webmaster will need to be responsible, accurate, energetic, highly organized, and keen to keep the site current. I believe that interactive discussions on these sites should be encouraged, and advisory committees must be willing to check frequently to ensure that all new information is accurate. Lastly, for the field of molecular epidemiology, the importance of correlating an informative genotype with an unequivocal phenotype is emphasized, and the emerging realization that racial and ethnic groups are highly admixed is summarized and updated.

Alleles↗
PubMed2004

On the integration of alcohol-related quantitative trait loci and gene expression analyses.

BACKGROUND: Quantitative trait loci (QTLs) have been detected for a wide variety of ethanol-related phenotypes, including acute and chronic ethanol withdrawal, acute locomotor activation, and ethanol preference. This study was undertaken to determine whether the process of moving from QTL to quantitative trait gene (QTG) could be accelerated by the integration of functional genomics (gene expression) into the analysis strategy. METHODS: Six ethanol-related QTLs, all detected in C57BL/6J and DBA/2J intercrosses were entered into the analysis. Each of the QTLs had been confirmed in independent genetic models at least once; the cumulative probabilities for QTL existence ranged from 10 to 10. Brain gene expression data for the C57BL/6 and DBA/2 strains (n = 6 per strain) and an F2 intercross sample (n = 56) derived from these strains were obtained by using the Affymetrix U74Av2 and 430A arrays; additional data with the U74Av2 array were available for the extended amygdala, dorsomedial striatum, and hippocampus. Low-level analysis was performed by using multiple methods to determine the likelihood that a transcript was truly differentially expressed. For the 430A array data, the F2 sample was used to determine which of the differentially expressed transcripts within the QTL intervals were cis-regulated and, thus, strong candidates for QTGs. RESULTS: Within the 6 QTL intervals, 39 transcripts (430A array) were identified as being highly likely to be differentially expressed between the C57BL/6 and DBA/2 strains at a false discovery rate of 0.01 or better. Twenty-eight of these transcripts showed significant (logarithm of odds > or =3.6) to highly significant (logarithm of odds >7) cis-regulation. The process correctly detected Mpdz (chromosome 4) as a candidate QTG for acute withdrawal. CONCLUSIONS: Although improvements are needed in the expression databases, the integration of QTL and gene expression analyses seems to have potential as a high-throughput strategy for moving from QTL to QTG.

Alcohol Drinking↗
PubMed2005

The biologic basis for neuroblastoma heterogeneity and risk stratification.

PURPOSE OF REVIEW: Neuroblastoma serves as the paradigm for the clinical utility of tumor-specific biologic data for prognostication. This review will describe the genetic and biologic basis for the diverse clinical phenotypes observed in neuroblastoma patients. It will also discuss the current approach to risk classification and how this may change in the future. RECENT FINDINGS: The biologic basis of neuroblastoma has come into clearer focus. PHOX2B is the first bona fide neuroblastoma predisposition gene identified, but is mutated in only a small subset of cases. Somatically acquired alterations at chromosome arms 3p and 11q are highly correlated with acquisition of metastases in the absence of MYCN amplification and may be useful as prognostic markers. The Children's Oncology Group risk classification system has been validated, with current emphasis on further refinement such as reevaluation of the age cutoff used to stratify therapy, and incorporation of additional molecular genetic markers is being studied prospectively. High-throughput genome scale analyses of neuroblastomas are further clarifying the genetic basis of this heterogeneous disease. SUMMARY: Neuroblastoma remains a significant challenge as high-risk patients are treated with intensive multimodal therapies but cure rates remain suboptimal. There is remarkable heterogeneity observed in tumor phenotype, ranging from spontaneous regression to relentless progression. There are literally dozens of clinical and biologic markers that have been proposed as being predictive of disease outcome, but large clinical correlative studies are sharpening the focus of which markers can be used by the clinician to optimize therapy for an individual patient.

Age Factors↗
PubMed2003

Detection of single-copy chromosome 17q gain in human neuroblastomas using real-time quantitative polymerase chain reaction.

Regional genomic alterations resulting from single-copy allelic loss or gain have been well characterized in many human cancers and are often of prognostic relevance. Unbalanced gain of 17q material is common in malignant human neuroblastomas and typically results from unbalanced translocations. Unbalanced 17q gain may be an independent predictor of disease outcome, but technical difficulties with quantifying such gain using fluorescent in situ hybridization gives this method limited clinical applicability. We now describe a duplex genomic DNA-based quantitative polymerase chain reaction assay to determine the presence or absence of unbalanced gain of chromosome 17q in primary neuroblastoma specimens. The technique was first refined and validated in a panel of nine human neuroblastoma-derived cell lines by direct comparison with dual-color fluorescent in situ hybridization. Prospective blinded comparison of quantitative polymerase chain reaction and fluorescence in situ hybridization in 40 human neuroblastoma primary tumor samples showed a sensitivity of 96% and 100% specificity for detecting unbalanced 17q gain when a relative 17q copy number ratio of 1.3 was used to define unbalanced gain. Tumors with ratios >1.3 were highly associated with malignant tumor phenotypic features such as metastatic disease (P <.0001) and tumor MYCN amplification (P =.008). These data suggest that quantitative polymerase chain reaction determination of 17q status is feasible and highly specific in primary tumor samples. Sensitivity may be limited because of the inherent complexity of both the chromosomal rearrangements and heterogeneity of some tumor samples. Taken together, quantitative polymerase chain reaction can be used as a high-throughput screening tool for 17q aberrations, but a subset of samples may also require fluorescence in situ hybridization analysis in an attempt to conclusively determine 17q allelic status.

Cell Line, Tumor↗
PubMed2002

Oligonucleotide arrays for high-throughput SNPs detection in the MHC class I genes: HLA-B as a model system.

A simple and efficient oligonucleotide array was developed to identify single nucleotide polymorphisms (SNPs) encoded within the highly polymorphic human major histocompatibility complex (MHC) using HLA-B as a model system. A total of 137 probes were designed to represent all known polymorphisms encoded in exons 2 and 3. PCR products were amplified from human genomic DNA and allowed to hybridize with the oligonucleotide array. Hybridization was detected by fluorescence scanning, and HLA-B alleles were assigned by quantitative analysis of the hybridization results. Variables known to influence the specificity of hybridization, such as oligonucleotide probe size, spacer length, surface density, hybridization conditions, and array uniformity and stability were studied. The efficiency and specificity of identifying HLA-B SNPs using the oligonucleotide arrays was evaluated by blinded analysis of 100 samples from unrelated individuals representing all HLA-B phenotypes. The oligonucleotide array method described in this paper provides unambiguous detection of complex heterozygous SNP combinations. This methodological approach may be applied to other highly polymorphic gene systems.

Alleles↗
PubMed2026

Performance of the IR Biotyper, Nanopore, and Illumina sequencing to discriminate Escherichia coli strains originating from poultry.

UNLABELLED: Escherichia coli is a highly diverse bacterial species that includes avian pathogenic E. coli (APEC), one of the most prevalent causative agents of disease in poultry worldwide. Rapid and accurate discrimination of E. coli strains is essential for outbreak management, antimicrobial resistance surveillance, and vaccine development. In this study, we compared the performance of Fourier Transform Infrared (FTIR) spectroscopy using the IR Biotyper system with Nanopore and Illumina whole-genome sequencing (WGS) for typing 200 E. coli isolates, originating from four poultry rearing farms in the Netherlands. From each farm, we sampled 10 one-day-old meat type rearing chicks, and from every chick, we isolated 5 E. coli strains. FTIR clustering showed strong concordance with WGS-based classifications, particularly serotyping and core-genome similarity determined by PopPUNK analysis (Adjusted Rand Index 0.75-0.92). While Nanopore and Illumina sequencing provided the highest genetic resolution, FTIR offered a faster (max 6 vs 12-28 days for 200 isolates) and more cost-effective alternative for assessing clonality. Across all methods, multiple strains were detected per farm, whereas most birds carried a single dominant E. coli strain. Our findings demonstrate that FTIR provides a reliable and scalable phenotypic method for rapid strain discrimination in E. coli, complementing WGS in diagnostic, surveillance, and epidemiological settings where speed and throughput are critical. IMPORTANCE: Escherichia coli is a major pathogen in poultry and a potential zoonotic risk for humans. Rapid and accurate discrimination of avian pathogenic E. coli (APEC) strains is critical for outbreak management, antimicrobial resistance surveillance, and the design of effective autogenous vaccines. In this study, we compared Fourier Transform Infrared (FTIR) spectroscopy with Nanopore and Illumina whole-genome sequencing for strain typing of E. coli isolates originating from poultry. The results show that FTIR provides comparable clustering accuracy to genomic approaches at a fraction of the time and costs. This work demonstrates that FTIR can serve as a practical, high-throughput alternative for routine monitoring of E. coli in veterinary diagnostics and food safety of poultry meat, enabling faster decision-making and more targeted interventions across the poultry production chain.

Animals↗
PubMed2025

Targeted next-generation sequencing for drug-resistant tuberculosis diagnosis: implementation considerations for bacterial load, regimen selection and diagnostic algorithm placement.

INTRODUCTION: Early and accurate diagnosis of drug-resistant tuberculosis (DR-TB) is essential for improving treatment outcomes. Phenotypic drug susceptibility testing (pDST) is comprehensive but slow, while rapid molecular assays provide resistance information for a limited number of drugs. Targeted next-generation sequencing (tNGS) offers the potential for broad and rapid resistance detection, but its integration into diagnostic algorithms has been hindered by uncertainty about its placement within existing workflows. METHODS: This study evaluated the extent to which two tNGS solutions-Deeplex Myc-TB (GenoScreen) and TB Drug Resistance Test (Oxford Nanopore Technologies, ONT)-provided interpretable drug resistance results that could inform regimen design, in comparison to other WHO-recommended molecular assays and pDST. Data were collected from three high-burden DR-TB settings under the Seq&Treat study. Sequencing success rates and drug resistance detection were analysed based on: (1) the initial Xpert MTB/RIF result (very low, low, medium, high), (2) resistance results for drugs in WHO-recommended regimens and (3) performance relative to other WHO-endorsed assays. The potential impact of different algorithms on the estimates was also considered. Key factors influencing successful tNGS adoption within diagnostic pathways were identified, leveraging insights from the Seq&Treat diagnostic accuracy study. RESULTS: Sequencing success rates were 88.5% (GenoScreen) and 93.1% (ONT) across 763 samples. While tNGS provided complete resistance data for 73%-86% of drugs in recommended regimens, pDST achieved 92%-93%. Both tNGS solutions matched or exceeded the sensitivity of WHO-recommended molecular assays. CONCLUSIONS: This study highlights the critical role of tNGS as a centralised tool for comprehensive drug resistance testing to inform DR-TB treatment decisions following initial screening assays. By complementing existing molecular tests with tNGS, diagnostic workflows can be optimised to ensure timely and comprehensive resistance detection. These findings support policy updates to integrate tNGS into global TB diagnostic algorithms. TRIAL REGISTRATION NUMBER: NCT04239326.

Humans↗
PubMed2005

Global analysis of uniparental disomy using high density genotyping arrays.

BACKGROUND: Uniparental disomy (UPD), the inheritance of both copies of a chromosome from a single parent, has been identified as the cause for congenital disorders such as Silver-Russell, Prader-Willi, and Angelman syndromes. Detection of UPD has largely been performed through labour intensive screening of DNA from patients and their parents, using microsatellite markers. METHODS: We applied high density single nucleotide polymorphism (SNP) microarrays to diagnose whole chromosome and segmental UPD and to study the occurrence of continuous or interspersed heterodisomic and isodisomic regions in six patients with Silver-Russell syndrome patients who had maternal UPD for chromosome 7 (matUPD7). RESULTS: We have devised a new high precision and high-throughput computational method to confirm UPD and to localise segments where transitions of UPD status occur. Our method reliably confirmed and mapped the matUPD7 regions in all patients in our study. CONCLUSION: Our results suggest that high density SNP arrays can be reliably used for rapid and efficient diagnosis of both segmental and whole chromosome UPD across the entire genome.

Chromosome Mapping↗
PubMed2005

'PACLIMS': a component LIM system for high-throughput functional genomic analysis.

BACKGROUND: Recent advances in sequencing techniques leading to cost reduction have resulted in the generation of a growing number of sequenced eukaryotic genomes. Computational tools greatly assist in defining open reading frames and assigning tentative annotations. However, gene functions cannot be asserted without biological support through, among other things, mutational analysis. In taking a genome-wide approach to functionally annotate an entire organism, in this application the approximately 11,000 predicted genes in the rice blast fungus (Magnaporthe grisea), an effective platform for tracking and storing both the biological materials created and the data produced across several participating institutions was required. RESULTS: The platform designed, named PACLIMS, was built to support our high throughput pipeline for generating 50,000 random insertion mutants of Magnaporthe grisea. To be a useful tool for materials and data tracking and storage, PACLIMS was designed to be simple to use, modifiable to accommodate refinement of research protocols, and cost-efficient. Data entry into PACLIMS was simplified through the use of barcodes and scanners, thus reducing the potential human error, time constraints, and labor. This platform was designed in concert with our experimental protocol so that it leads the researchers through each step of the process from mutant generation through phenotypic assays, thus ensuring that every mutant produced is handled in an identical manner and all necessary data is captured. CONCLUSION: Many sequenced eukaryotes have reached the point where computational analyses are no longer sufficient and require biological support for their predicted genes. Consequently, there is an increasing need for platforms that support high throughput genome-wide mutational analyses. While PACLIMS was designed specifically for this project, the source and ideas present in its implementation can be used as a model for other high throughput mutational endeavors.

Algorithms↗
PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗
PubMed2004

A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Amino Acid Sequence↗
PubMed2004

The role of emerging genomics and proteomics technologies in cancer drug target discovery.

Cancer drugs have traditionally been identified in screens designed to produce broad biological end points such as cell death. A serious undesired outcome of drugs discovered in these screens is that the mechanism of drug action is unknown and such drugs often have adverse side effects. Designing cancer drugs that act on specific targets offer the advantage that the mechanism of drug action can be understood and accurately monitored in clinical trials leading to development of better drugs. The pharmacological industry has recently shifted to a target directed drug discovery model. However, until recently potential cancer drug targets comprised of only a small fraction of the human genome. The human genome project and high-throughput structural and functional genomics have dramatically increased the number of cancer drug targets. Deciphering cancer drug targets requires the understanding of biochemical pathways that are affected in the cancer genome. It has been suggested that utilization of Single-nucleotide polymorphisms (SNPs) will aid in identifying individuals at high risk of developing certain cancers, and will also help in development of tailored medication or identify genetic profiles of specific drug action and toxicity. Achieving successful new cancer drug development schemes will require a merger of research disciplines that include pharmacology, genomics, comparative genomics, functional genomics, proteomics and bioinformatics. In this review the significance and challenges of these rapidly evolving technologies in cancer drug target discovery are discussed.

Animals↗