Functional analysis of new genes is playing a central role in postgenomic era. Here we reviewed several main strategies including bioinformatics, gene transduction, antisense technology, certain gene silence induced by RNA interference (RNAi), transgene and gene knockout and artificial chromosome transduction.
Animals ; Computational Biology ; methods ; Genes ; physiology ; Humans ; Transduction, Genetic ; methods

Biosequence analysis is the primary research field of bioinformatics. In this field, useful information can be extracted by comparison analysis methods. Among them, sequence alignment is the most common comparison method. However the sequence comparison by alignment, which assumes conservation of contiguity between homologous segments, is at odds with genetic recombination. Especially for the multisequence alignment, there exists the difficulty in the complexity of calculation. Therefore, alignment-free sequence comparison methods are required. In this paper, two main categories of alignment-free sequence comparison methods are reviewed. The first one is based on the word (oligomer) frequency and its distribution. The sequences are compared using the distances defined in a Cartesian space by the frequency vectors. In the second category, sequences are compared using Kolmogorov complexity and chaos theory.
Algorithms ; Computational Biology ; Sequence Alignment ; Sequence Analysis ; methods

Asthma and allergic disease are of multifactorial nature like most of the other diseases that clinicians are facing. To establish the disease nature and improve the treatment success rate, it is unavoidable to examine closely enormous clinical and biological data that have been accumulated during the last century. The expanding gap between basic research and clinical medicine demand a novel approach. System biology emerged to reduce this gap as an interdisciplinary and translational method, and integrated clinical and experimental data through bioinformatics and mathematical modeling. Mathematical modeling is the method that disassembles the system, interpret the complex relations concealed among elements, and then establish a comprehensive and testable new hypothesis for the complex phenomenon or disease. To this end, we review the mathematical models dealting with asthma and immunologic system.
Allergy and Immunology ; Asthma* ; Biology ; Clinical Medicine ; Computational Biology ; Methods ; Models, Theoretical*

A disease is rarely caused by a single virulence gene, but by an imbalanced regulatory network arising from dysfunction of multiple genes or their products. However, drugs intervene the occurrence and development of a disease by acting on multiple target points in the disease network and making a synergy effect on each target point, in order to achieve the therapeutic effect. Unlike traditional approaches focusing on a single molecule or pathway, network analysis with high-throughput data provides a new perspective for studying disease pathobiology and pharmacological mechanisms, and brings forth new ideas for multi-component and multi-target-point pharmacologic mechanisms of traditional Chinese medicines, in three aspects-establishment of relevant disease and drug network, network decomposition, and biological significant of sub-network.
Computational Biology ; methods ; Disease ; Drug Therapy ; methods ; Humans ; Medicine, Chinese Traditional ; Pharmacology ; methods

Proteinchip profiling is a powerful and innovative proteomic technology for biomarker discovery and diagnostic/prognostic assay development. Based on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen's proteinchip system offers a single, unified, high-throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell, the study of proteomics provides mankind with a better understanding of disease and life. The remarkable findings in disease biomarkers have shed light to the early diagnosis, monitoring and predicting prognosis of various diseases, especially for cancer. In this article, the development and technology of SELDI-TOF MS are introduced. Some research progress and encouraging research results in oncoproteomics, infectious diseases, neurological diseases and diabetes mellitus using SELDI-TOF MS are also reviewed. The paper is closed by the appraisals on its pros and cons, as well as the future prospective is also expounded.
Animals ; Computational Biology ; Humans ; Lasers ; Mass Spectrometry ; methods ; Protein Array Analysis ; methods ; Proteomics ; Surface Properties

OBJECTIVETo analyze the specifically expressed genes in sperms for better understanding of the molecular characteristics of sperms.

METHODSThe hybridization data the genes in the sperms, oocytes and 10 normal tissues were retrieved from the GEO database to identify the genes expressed specifically in sperms and the patterns of their regulation using such bioinformatic tools as GATHER, PANTHER and DAVID.

RESULTS AND CONCLUSIONSComparison of the spermatozoal gene expression profiles with those of the normal tissues identified 8998 differentially expressed probes, among which 25 genes were up-regulated by over 200 folds in the sperms. Comparison of the gene expression profiles between the oocytes and normal tissues resulted in the identification of 8981 differentially expressed probes. Of the 1709 up-regulated genes in the sperm with a ratio>5, 1218 genes showed similar expressions in the oocytes and the normal tissues, and 129 were up-regulated and 362 down-regulated in the oocytes. The 362 genes up-regulated in the sperms but down-regulated in the oocytes were involved mainly in protein modification and metabolism and nucleic acid metabolism, but very few participated in the intracellular signaling pathways. Numerous transcriptional factors containing the KRAB domain and receptor- independent serine/threonine kinase were specifically overexpressed in sperms, and the a very high proportion of the genes specifically overexpressed in the sperms coincided with the overexpressed genes in the neural stem cells and embryonic stem cells. The genes involved in the glycolysis were down-regulated in the sperms. These findings in the genes specifically expressed in the sperms by data mining using bioinformatic methods may provide better insight into the molecular characteristics of the sperms.

Adult ; Computational Biology ; methods ; Data Mining ; Gene Expression Profiling ; methods ; Humans ; Male ; Spermatozoa ; cytology

To explore a new method for analyzing biological molecules that have already been sequenced, an experimental study on an auditory method was carried out. The auditory method for analyzing biological molecules includes audible representation of sequence data. Audible representation of sequence data was implemented by using a multimedia computer. Each mononucleotide in a DNA sequence was matched with a corresponding sound, i.e., a DNA sequence was "dubbed" in a sound sequence. When the sound sequence is played, a special cadence can be heard. In the audible representation experiment, special cadences of different exons can be clearly heard. The results show that audible representation of DNA sequence data can be implemented by using a multimedia technique. After a 5-time auditory training, subjects both in internal testing and external testing can obtain 93%-100% of judgment accuracy rate for the difference between two sound sequences of two different exons, thus providing an experimental basis for the practicability of this method. Auditory method for analyzing DNA segments might be beneficial for the research in comparative genomics and functional genomics. This new technology must be robust and be carefully evaluated and improved in a high-throughput environment before its implementation in an application setting.
Computational Biology ; methods ; Decision Making, Computer-Assisted ; Genome ; Humans ; Multimedia ; Sequence Analysis, DNA ; methods ; Sound

A new mutil-classification method based on binary tree SVM (BT-SVM) is presented to predict protein structural class. The protein sequence, which is represented by 26-D vector, is used as input vector. BT-SVM method resolves unclassifiable regions for multiclass problems which can not be solved by SVM. Self-consistency and cross validation test are used to verify the performance of the proposal method on two benchmark datasets. Satisfactory test results demonstrate that the new method is promising. The Jackknife results of the new method are compared with the existing results on the same datasets. The results of the new method are almost the same as the ones of the best exiting method. It illuminates that the new method has good prediction performance and it will become a useful tool in protein structure class prediction.
Computational Biology ; methods ; Humans ; Predictive Value of Tests ; Protein Structure, Secondary ; Proteins ; chemistry ; Sequence Analysis, Protein ; methods

We developed an R function named "microarray outlier filter" (MOF) to assist in the identification of failed arrays. In sorting a group of similar arrays by the likelihood of failure, two statistical indices were employed: the correlation coefficient and the percentage of outlier spots. MOF can be used to monitor the quality of microarray data for both trouble shooting, and to eliminate bad datasets from downstream analysis. The function is freely avaliable at http://www.wriwindber.org/applications/mof/.
Algorithms ; Computational Biology ; methods ; Internet ; Oligonucleotide Array Sequence Analysis ; methods ; statistics & numerical data ; Reproducibility of Results

With the current accumulation of metagenome data, it is possible to build an integrated platform for processing of rigorously selected metagenomic samples (also referred as "metagenomic communities" here) of interests. Any metagenomic samples could then be searched against this database to find the most similar sample(s). However, on one hand, current databases with a large number of metagenomic samples mostly serve as data repositories but not well annotated database, and only offer few functions for analysis. On the other hand, the few available methods to measure the similarity of metagenomic data could only compare a few pre-defined set of metagenome. It has long been intriguing scientists to effectively calculate similarities between microbial communities in a large repository, to examine how similar these samples are and to find the correlation of the meta-information of these samples. In this work we propose a novel system, Meta-Mesh, which includes a metagenomic database and its companion analysis platform that could systematically and efficiently analyze, compare and search similar metagenomic samples. In the database part, we have collected more than 7 000 high quality and well annotated metagenomic samples from the public domain and in-house facilities. The analysis platform supplies a list of online tools which could accept metagenomic samples, build taxonomical annotations, compare sample in multiple angle, and then search for similar samples against its database by a fast indexing strategy and scoring function. We also used case studies of "database search for identification" and "samples clustering based on similarity matrix" using human-associated habitat samples to demonstrate the performance of Meta-Mesh in metagenomic analysis. Therefore, Meta-Mesh would serve as a database and data analysis system to quickly parse and identify similar
Cluster Analysis ; Computational Biology ; methods ; Databases, Genetic ; Humans ; Metagenome ; Metagenomics ; methods