Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge
amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not
provided in a biologically significant way for cross‐analysis and ‐comparison, thus limiting its application.
Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for
cross‐analyzing gene expression data among different SAGE libraries of tissue sources; up‐ or down‐regulated
tissue‐specific tags can be identified computationally. Extract‐SAGE employs a genetic algorithm (GA) to reduce the
number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with
discriminating gene expression.
This software and user manual are freely available at
SAGE; genetic algorithm; set theory; software
Alzheimer's disease (AD) is the main cause of dementia for older people. Although several antidementia drugs such as donepezil, rivastigmine, galantamine, and memantine have been developed, the effectiveness of AD drug therapy is still far from satisfactory. Recently, the single nucleotide polymorphisms (SNPs) have been chosen as one of the personalized medicine markers. Many pharmacogenomics databases have been developed to provide comprehensive information by associating SNPs with drug responses, disease incidence, and genes that are critical in choosing personalized therapy. However, we found that some information from different sets of pharmacogenomics databases is not sufficient and this may limit the potential functions for pharmacogenomics. To address this problem, we used approximate string matching method and data mining approach to improve the searching of pharmacogenomics database. After computation, we can successfully identify more genes linked to AD and AD-related drugs than previous online searching. These improvements may help to improve the pharmacogenomics of AD for personalized medicine.
In addition to the previous investigations of bioactivity of aqueous extract of the edible Gracilaria tenuistipitata (AEGT) against H2O2-induced DNA damage and hepatitis C virus replication, the purpose of this study is to evaluate the potential therapeutic properties of AEGT against inflammation and hepatotoxicity using lipopolysaccharide (LPS)-stimulated mouse RAW 264.7 cells, primary rat peritoneal macrophages and carbon tetrachloride (CCl4)-induced acute hepatitis model in rats. AEGT concentration-dependently inhibited the elevated RNA and protein levels of inducible nitric oxide synthase and cyclooxygenase-2, thereby reducing nitric oxide and prostaglandin E2 levels, respectively. Moreover, AEGT significantly suppressed the production of LPS-induced proinflammatory cytokines, including interleukin (IL)-1β, IL-6 and tumor necrosis factor-α. These inhibitory effects were associated with the suppression of nuclear factor-kappa B activation and mitogen-activated protein kinase phosphorylation by AEGT in LPS-stimulated cells. In addition, we highlighted the hepatoprotective and curative effects of AEGT in a rat model of CCl4-intoxicated acute liver injury, which was evident from reduction in the elevated serum aspartate aminotransferase and alanine aminotransferase levels as well as amelioration of histological damage by pre-treatment or post-treatment of AEGT. In conclusion, the results demonstrate that AEGT may serve as a potential supplement in the prevention or amelioration of inflammatory diseases.
Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small.
To address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1∶4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR.
Results showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets.
This study computationally determines the contribution of clinicopathologic factors correlated with 5-year survival in oral squamous cell carcinoma (OSCC) patients primarily treated by surgical operation (OP) followed by other treatments. From 2004 to 2010, the program enrolled 493 OSCC patients at the Kaohsiung Medical Hospital University. The clinicopathologic records were retrospectively reviewed and compared for survival analysis. The Apriori algorithm was applied to mine the association rules between these factors and improved survival. Univariate analysis of demographic data showed that grade/differentiation, clinical tumor size, pathology tumor size, and OP grouping were associated with survival longer than 36 months. Using the Apriori algorithm, multivariate correlation analysis identified the factors that coexistently provide good survival rates with higher lift values, such as grade/differentiation = 2, clinical stage group = early, primary site = tongue, and group = OP. Without the OP, the lift values are lower. In conclusion, this hospital-based analysis suggests that early OP and other treatments starting from OP are the key to improving the survival of OSCC patients, especially for early stage tongue cancer with moderate differentiation, having a better survival (>36 months) with varied OP approaches.
Long noncoding RNA (lncRNA) function is described in terms of related gene expressions, diseases, and cancers as well as their polymorphisms. Potential modulators of lncRNA function, including clinical drugs, natural products, and derivatives, are discussed, and bioinformatic resources are summarized. The improving knowledge of the lncRNA regulatory network has implications not only in gene expression, diseases, and cancers, but also in the development of lncRNA-based pharmacology.
Alternative splicing is a major diversification mechanism in the human transcriptome and proteome. Several diseases, including cancers, have been associated with dysregulation of alternative splicing. Thus, correcting alternative splicing may restore normal cell physiology in patients with these diseases. This paper summarizes several alternative splicing-related diseases, including cancers and their target genes. Since new cancer drugs often target spliceosomes, several clinical drugs and natural products or their synthesized derivatives were analyzed to determine their effects on alternative splicing. Other agents known to have modulating effects on alternative splicing during therapeutic treatment of cancer are also discussed. Several commonly used bioinformatics resources are also summarized.
RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently, RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products. In this review, we provide a literature overview of the biological functions of RNA editing on gene expression, diseases, cancers, and drugs. The bioinformatics resources of RNA editing were also summarized.
Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex traits; the effects of SNP-SNP interaction need to be characterized. Furthermore, observations of the complex interplay (interactions) between SNPs for high-dimensional combinations are still computationally and methodologically challenging. An improved branch and bound algorithm with feature selection (IBBFS) is introduced to identify SNP combinations with a maximal difference of allele frequencies between the case and control groups in breast cancer, i.e., the high/low risk combinations of SNPs.
A total of 220 real case and 334 real control breast cancer data are used to test IBBFS and identify significant SNP combinations. We used the odds ratio (OR) as a quantitative measure to estimate the associated cancer risk of multiple SNP combinations to identify the complex biological relationships underlying the progression of breast cancer, i.e., the most likely SNP combinations. Experimental results show the estimated odds ratio of the best SNP combination with genotypes is significantly smaller than 1 (between 0.165 and 0.657) for specific SNP combinations of the tested SNPs in the low risk groups. In the high risk groups, predicted SNP combinations with genotypes are significantly greater than 1 (between 2.384 and 6.167) for specific SNP combinations of the tested SNPs.
This study proposes an effective high-speed method to analyze SNP-SNP interactions in breast cancer association studies. A number of important SNPs are found to be significant for the high/low risk group. They can thus be considered a potential predictor for breast cancer association.
An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.
Antrodia camphorata (AC) is well known in Taiwan as a traditional Chinese medicine. The aim of this study was to investigate whether a fermented culture broth of AC could inhibit melanoma proliferation and progression via suppression of the Wnt/β-catenin signaling pathway. In this study, we observed that AC treatment resulted in decreased cell viability and disturbed Wnt/β-catenin cascade in B16F10 and/or B16F1 melanoma cells. This result was accompanied by a decrease in the expression of Wnt/β-catenin transcriptional targets, including c-Myc and survivin. Furthermore, treatment of melanoma cells with AC resulted in a significant increase in apoptosis, which was associated with DNA fragmentation, cytochrome c release, caspase-9 and -3 activation, PARP degradation, Bcl-2/Bax dysregulation, and p53 expression. We also observed that AC caused G1 phase arrest mediated by a downregulation of cyclin D1 and CDK4 and increased p21 and p27 expression. In addition, we demonstrated that non- and subcytotoxic concentrations of AC markedly inhibited migration and invasion of highly metastatic B16F10 cells. The antimetastatic effect of AC was further confirmed by reductions in the levels of MMP-2, MMP-9, and VEGF expression. These results suggest that Antrodia camphorata may exert antitumor activity by downregulating the Wnt/β-catenin pathways.
The mitogen-activated protein kinase (MAPK) phosphatase-1 (MKP-1) belongs to the MAPK cascades which are central to cell proliferation and apoptosis. The carcinogenic role of MKP-1 has been reported in many types of cancer but it has rarely been investigated in breast cancer. The present study was designed to evaluate the MKP-1 mRNA expression and its possible regulation by methylation of MKP-1 promoter in the model of several breast cancer cell lines and tissues as well as controls. Our data demonstrate MKP-1 mRNA expression significantly decreased in five breast cancer cell lines compared to breast controls (P < 0.01). Using the methylation-specific PCR (MSP) analysis, the unmethylated reaction (U) is dominant in both normal cell lines and benign breast tumors (100% vs. 86.2%), whereas the methylated reaction (M) is dominant in both breast cancer cell lines and invasive breast tumors (100% vs. 57.2%). In terms of methylation ratio (M/M+U), methylation level in MKP-1 promoter is significantly higher in the invasive breast tumor tissues (n = 152) than in benign breast tumor tissues (n = 29) (P < 0.0001). Assessing the methylation ratio of the promoter of MKP-1 gene to diagnose the breast malignancy (invasive vs. benign), the area under the receiver-operating characteristic (ROC) curve was 0.809 (95% CI: 0.711-0.906, P < 0.001). The best performance for this prediction has a sensitivity of 76.32% and a specificity of 82.76% at the cutoff value of 0.38. Taken together, we firstly demonstrated that the promoter methylation of MKP-1 gene is a potential breast cancer biomarker for breast malignancy.
biological markers; breast neoplasms; DNA methylation; DUSP1 protein, human; epigenesis, genetic
Complete mitochondrial (mt) genome sequencing is becoming increasingly common for phylogenetic reconstruction and as a model for genome evolution. For long template sequencing, i.e., like the entire mtDNA, it is essential to design primers for Polymerase Chain Reaction (PCR) amplicons which are partly overlapping each other. The presented chromosome walking strategy provides the overlapping design to solve the problem for unreliable sequencing data at the 5′ end and provides the effective sequencing. However, current algorithms and tools are mostly focused on the primer design for a local region in the genomic sequence. Accordingly, it is still challenging to provide the primer sets for the entire mtDNA.
The purpose of this study is to develop an integrated primer design algorithm for entire mt genome in general, and for the common primer sets for closely-related species in particular. We introduce ClustalW to generate the multiple sequence alignment needed to find the conserved sequences in closely-related species. These conserved sequences are suitable for designing the common primers for the entire mtDNA. Using a heuristic algorithm particle swarm optimization (PSO), all the designed primers were computationally validated to fit the common primer design constraints, such as the melting temperature, primer length and GC content, PCR product length, secondary structure, specificity, and terminal limitation. The overlap requirement for PCR amplicons in the entire mtDNA is satisfied by defining the overlapping region with the sliding window technology. Finally, primer sets were designed within the overlapping region. The primer sets for the entire mtDNA sequences were successfully demonstrated in the example of two closely-related fish species. The pseudo code for the primer design algorithm is provided.
In conclusion, it can be said that our proposed sliding window-based PSO algorithm provides the necessary primer sets for the entire mt genome amplification and sequencing.
SAGE (serial analysis of gene expression) is a powerful method of analyzing gene expression for the entire transcriptome. There are currently many well-developed SAGE tools. However, the cross-comparison of different tissues is seldom addressed, thus limiting the identification of common- and tissue-specific tumor markers.
To improve the SAGE mining methods, we propose a novel function for cross-tissue comparison of SAGE data by combining the mathematical set theory and logic with a unique “multi-pool method” that analyzes multiple pools of pair-wise case controls individually. When all the settings are in “inclusion”, the common SAGE tag sequences are mined. When one tissue type is in “inclusion” and the other types of tissues are not in “inclusion”, the selected tissue-specific SAGE tag sequences are generated. They are displayed in tags-per-million (TPM) and fold values, as well as visually displayed in four kinds of scales in a color gradient pattern. In the fold visualization display, the top scores of the SAGE tag sequences are provided, along with cluster plots. A user-defined matrix file is designed for cross-tissue comparison by selecting libraries from publically available databases or user-defined libraries.
The hSAGEing tool provides a combination of friendly cross-tissue analysis and an interface for comparing SAGE libraries for the first time. Some up- or down-regulated genes with tissue-specific or common tumor markers and suppressors are identified computationally. The tool is useful and convenient for in silico cancer transcriptomic studies and is freely available at http://bio.kuas.edu.tw/hSAGEing
Polymerase chain reaction with confronting two-pair primers (PCR-CTPP) method produces allele-specific DNA bands of different lengths by adding four designed primers and it achieves the single nucleotide polymorphism (SNP) genotyping by electrophoresis without further steps. It is a time- and cost-effective SNP genotyping method that has the advantage of simplicity. However, computation of feasible CTPP primers is still challenging.
In this study, we propose a GA (genetic algorithm)-based method to design a feasible CTPP primer set to perform a reliable PCR experiment. The SLC6A4 gene was tested with 288 SNPs for dry dock experiments which indicated that the proposed algorithm provides CTPP primers satisfied most primer constraints. One SNP rs12449783 in the SLC6A4 gene was taken as an example for the genotyping experiments using electrophoresis which validated the GA-based design method as providing reliable CTPP primer sets for SNP genotyping.
The GA-based CTPP primer design method provides all forms of estimation for the common primer constraints of PCR-CTPP. The GA-CTPP program is implemented in JAVA and a user-friendly input interface is freely available at http://bio.kuas.edu.tw/ga-ctpp/.
PCR-restriction fragment length polymorphism (RFLP) assay is a cost-effective method for SNP genotyping and mutation detection, but the manual mining for restriction enzyme sites is challenging and cumbersome. Three years after we constructed SNP-RFLPing, a freely accessible database and analysis tool for restriction enzyme mining of SNPs, significant improvements over the 2006 version have been made and incorporated into the latest version, SNP-RFLPing 2.
The primary aim of SNP-RFLPing 2 is to provide comprehensive PCR-RFLP information with multiple functionality about SNPs, such as SNP retrieval to multiple species, different polymorphism types (bi-allelic, tri-allelic, tetra-allelic or indels), gene-centric searching, HapMap tagSNPs, gene ontology-based searching, miRNAs, and SNP500Cancer. The RFLP restriction enzymes and the corresponding PCR primers for the natural and mutagenic types of each SNP are simultaneously analyzed. All the RFLP restriction enzyme prices are also provided to aid selection. Furthermore, the previously encountered updating problems for most SNP related databases are resolved by an on-line retrieval system.
The user interfaces for functional SNP analyses have been substantially improved and integrated. SNP-RFLPing 2 offers a new and user-friendly interface for RFLP genotyping that can be used in association studies and is freely available at http://bio.kuas.edu.tw/snp-rflping2.
Epstein-Barr virus is known to cause nasopharyngeal carcinoma. Although oral cavity is located close to the nasal pharynx, the pathogenetic role of Epstein-Barr virus (EBV) in oral cancers is unclear. This molecular epidemiology study uses EBV genomic microarray (EBV-chip) to simultaneously detect the prevalent rate and viral gene expression patterns in 57 oral squamous cell carcinoma biopsies (OSCC) collected from patients in Taiwan. The majority of the specimens (82.5%) were EBV-positive that probably expressed coincidently the genes for EBNAs, LMP2A and 2B, and certain structural proteins. Importantly, the genes fabricated at the spots 61 (BBRF1, BBRF2, and BBRF3) and 68 (BDLF4 and BDRF1) on EBV-chip were actively expressed in a significantly greater number of OSCC exhibiting exophytic morphology or ulceration than those tissues with deep invasive lesions (P = .0265 and .0141, resp.). The results may thus provide the lead information for understanding the role of EBV in oral cancer pathogenesis.
Linkage disequilibrium (LD) mapping is commonly used to evaluate markers for genome-wide association studies. Most types of LD software focus strictly on LD analysis and visualization, but lack supporting services for genotyping.
We developed a freeware called LD2SNPing, which provides a complete package of mining tools for genotyping and LD analysis environments. The software provides SNP ID- and gene-centric online retrievals for SNP information and tag SNP selection from dbSNP/NCBI and HapMap, respectively. Restriction fragment length polymorphism (RFLP) enzyme information for SNP genotype is available to all SNP IDs and tag SNPs. Single and multiple SNP inputs are possible in order to perform LD analysis by online retrieval from HapMap and NCBI. An LD statistics section provides D, D', r2, δQ, ρ, and the P values of the Hardy-Weinberg Equilibrium for each SNP marker, and Chi-square and likelihood-ratio tests for the pair-wise association of two SNPs in LD calculation. Finally, 2D and 3D plots, as well as plain-text output of the results, can be selected.
LD2SNPing thus provides a novel visualization environment for multiple SNP input, which facilitates SNP association studies. The software, user manual, and tutorial are freely available at .
Epidemiological surveillance of infectious diseases through the mandatory-reporting system is crucial in the planning and evaluation of disease control and prevention program. This study investigated the reporting behavior, knowledge, and attitude to reporting communicable disease in private doctors in Taiwan. The differences between the reporting and non-reporting doctors were also explored.
A total of 1250 clinics were randomly sampled nationwide by a 2-stage process. Data were collected from 1093 private doctors (87.4% response rate) using a self-administered structured questionnaire. Four hundred and six (37.2%) doctors reported having diagnosed reportable communicable diseases. Among them, 340 (83.5%) have the experiences of reporting.
The most common reasons for not reporting were "do not want to violate the patient's privacy", "reporting procedure is troublesome", and "not sure whether the diagnosed disease is reportable". Significantly higher proportions of the non-reporting doctors considered the reporting system inconvenient or were not familiar with the system. The highest percentage (65.2%) of the non-reporting doctors considered that a simplified reporting procedure, among all measures, would increase their willingness to report. In addition, a significantly higher proportion of the non-reporting doctors would increase their willingness to report if there has been a good reward for reporting or a penalty for not reporting.
The most effective way to improve reporting rate may be to modify doctor's attitude to disease reporting. The development of a convenient and widely-accepted reporting system and the establishment of a reward/penalty system may be essential in improving disease reporting compliance in private doctors.
Combination of CHD (chromo-helicase-DNA binding protein)-specific polymerase chain reaction (PCR) with electrophoresis (PCR/electrophoresis) is the most common avian molecular sexing technique but it is lab-intensive and gel-required. Gender determination often fails when the difference in length between the PCR products of CHD-Z and CHD-W genes is too short to be resolved.
Here, we are the first to introduce a PCR-melting curve analysis (PCR/MCA) to identify the gender of birds by genomic DNA, which is gel-free, quick, and inexpensive. Spilornis cheela hoya (S. c. hoya) and Pycnonotus sinensis (P. sinensis) were used to illustrate this novel molecular sexing technique. The difference in the length of CHD genes in S. c. hoya and P. sinensis is 13-, and 52-bp, respectively. Using Griffiths' P2/P8 primers, molecular sexing failed both in PCR/electrophoresis of S. c. hoya and in PCR/MCA of S. c. hoya and P. sinensis. In contrast, we redesigned sex-specific primers to yield 185- and 112-bp PCR products for the CHD-Z and CHD-W genes of S. c. hoya, respectively, using PCR/MCA. Using this specific primer set, at least 13 samples of S. c. hoya were examined simultaneously and the Tm peaks of CHD-Z and CHD-W PCR products were distinguished.
In this study, we introduced a high-throughput avian molecular sexing technique and successfully applied it to two species. This new method holds a great potential for use in high throughput sexing of other avian species, as well.
The flanking sequences provided by dbSNP of NCBI are usually short and fixed length without further extension, thus making
the design of appropriate PCR primers difficult. Here, we introduce a tool named “SNP-Flankplus” to provide a web environment
for retrieval of SNP flanking sequences from both the dbSNP and the nucleotide databases of NCBI. Two SNP ID types, rs# and
ss#, are acceptable for querying SNP flanking sequences with adjustable lengths for at least sixteen organisms.
This software is freely available at
PCR; SNP; primer design; flanking sequences
Mitochondrial single nucleotide polymorphisms (mtSNPs) constitute important data when trying to shed some light on human diseases and cancers. Unfortunately, providing relevant mtSNP genotyping information in mtDNA databases in a neatly organized and transparent visual manner still remains a challenge. Amongst the many methods reported for SNP genotyping, determining the restriction fragment length polymorphisms (RFLPs) is still one of the most convenient and cost-saving methods. In this study, we prepared the visualization of the mtDNA genome in a way, which integrates the RFLP genotyping information with mitochondria related cancers and diseases in a user-friendly, intuitive and interactive manner. The inherent problem associated with mtDNA sequences in BLAST of the NCBI database was also solved.
V-MitoSNP provides complete mtSNP information for four different kinds of inputs: (1) color-coded visual input by selecting genes of interest on the genome graph, (2) keyword search by locus, disease and mtSNP rs# ID, (3) visualized input of nucleotide range by clicking the selected region of the mtDNA sequence, and (4) sequences mtBLAST. The V-MitoSNP output provides 500 bp (base pairs) flanking sequences for each SNP coupled with the RFLP enzyme and the corresponding natural or mismatched primer sets. The output format enables users to see the SNP genotype pattern of the RFLP by virtual electrophoresis of each mtSNP. The rate of successful design of enzymes and primers for RFLPs in all mtSNPs was 99.1%. The RFLP information was validated by actual agarose electrophoresis and showed successful results for all mtSNPs tested. The mtBLAST function in V-MitoSNP provides the gene information within the input sequence rather than providing the complete mitochondrial chromosome as in the NCBI BLAST database. All mtSNPs with rs number entries in NCBI are integrated in the corresponding SNP in V-MitoSNP.
V-MitoSNP is a web-based software platform that provides a user-friendly and interactive interface for mtSNP information, especially with regard to RFLP genotyping. Visual input and output coupled with integrated mtSNP information from MITOMAP and NCBI make V-MitoSNP an ideal and complete visualization interface for human mtSNPs association studies.
The restriction fragment length polymorphism (RFLP) is a common laboratory method for the genotyping of single nucleotide polymorphisms (SNPs). Here, we describe a web-based software, named SNP-RFLPing, which provides the restriction enzyme for RFLP assays on a batch of SNPs and genes from the human, rat, and mouse genomes.
Three user-friendly inputs are included: 1) NCBI dbSNP "rs" or "ss" IDs; 2) NCBI Entrez gene ID and HUGO gene name; 3) any formats of SNP-in-sequence, are allowed to perform the SNP-RFLPing assay. These inputs are auto-programmed to SNP-containing sequences and their complementary sequences for the selection of restriction enzymes. All SNPs with available RFLP restriction enzymes of each input genes are provided even if many SNPs exist. The SNP-RFLPing analysis provides the SNP contig position, heterozygosity, function, protein residue, and amino acid position for cSNPs, as well as commercial and non-commercial restriction enzymes.
This web-based software solves the input format problems in similar softwares and greatly simplifies the procedure for providing the RFLP enzyme. Mixed free forms of input data are friendly to users who perform the SNP-RFLPing assay. SNP-RFLPing offers a time-saving application for association studies in personalized medicine and is freely available at .
For their various bioactivities, biomaterials derived from marine algae are important ingredients in many products, such as cosmetics and drugs for treating cancer and other diseases. This mini-review comprehensively compares the bioactivities and biological functions of biomaterials from red, green, brown, and blue-green algae. The anti-oxidative effects and bioactivities of several different crude extracts of algae have been evaluated both in vitro and in vivo. Natural products derived from marine algae protect cells by modulating the effects of oxidative stress. Because oxidative stress plays important roles in inflammatory reactions and in carcinogenesis, marine algal natural products have potential for use in anti-cancer and anti-inflammatory drugs.
Algae; ROS; Antioxidant; Inflammation; Antinociceptive; Anti-cancer