Current Bioinformatics - Volume 10, Issue 1, 2015

It is well known that the mutations in BRCA1 or BRCA2 gene can cause the hereditary breast cancer. However, it is a tedious and expensive task to identify the mutant genes that impact breast cancer due to the large number of genes and very small number of samples. Furthermore, the expressive energy of the subset of genes in comparison to that of one individual gene at a time is considered to have a profound influence in case of breast cancer. In this paper 7 tumors with BRCA1 mutation and 8 tumors with BRCA2 mutation have been used to identify the subset of discriminative genes. A combination of a non-parametric supervised and an unsupervised statistical method is introduced to analyze the gene expressions and the distinctive genes among the highly expressed genes are identified. The most important genes are filtered using the area under the curve (AUC) measure. These filtered genes are then used to build a hidden Markov model (HMM) to analyse their inter-relationship and identify the best subset among them. In addition, Protein-Protein interaction network is generated to analyse the pathways of the identified genes and their link with BRCA1 or BRCA2. Transcription Factors are identified and Gene Set Enrichment Analysis (GSEA) is calculated for the identified genes subset and the results are compared with the results mentioned in other cancer literature. Experimental results suggest that only 8 genes have been identified out of 3226 genes by the proposed hybrid method. Out of the 8 identified genes, 5 have been linked with breast cancer by other studies. Moreover, 7 genes have been associated with numerous diseases that may result in breast cancer. Furthermore, 8 transcription factors were identified that cover the identified genes and BRCA1 and BRCA2. Lastly, GSEA enrichment score of 0.52 is calculated for the identified genes and it is comparatively better considering the small subset of identified genes.

Classification for enzymes is essential for understanding the functions of organisms because it is directly related to the knowledge about which specific target it acts on, as well as to its catalytic process. Hence, it is crucial to use an accurate and robust approach to correctly map the enzyme in specific enzyme family class that it is belong to. In this research, a protein-protein interaction (PPI) network-based method was developed for predicting the families of enzyme. As a demonstration, the overall success rate by the jackknife test in identifying enzyme’s family class was 62.86%. The promising results imply that the predictor as presented in this paper may become a useful tool for studying enzymes.

Prediction and identification of cancer related genes are among of the most challenging and important problems in bioinformatics and biomedicine. Colorectal cancer (CRC), the second most commonly diagnosed cancer worldwide, is a major cause of cancer-related death. Knowledge of CRC-related genes may help to make an early detection of CRC and develop gene-targeted treatment schemes to significantly improve a patient’s prognosis and reduce the mortality. The very first and basic steps one needs to take are the screening and identification of CRC-related genes. Here, we presented a computational method to predict CRC-related genes based on JRip, a rule abstracting algorithm, and optimized its data inputs by the maximum relevance minimum redundancy (mRMR) method and incremental feature selection (IFS). 77 genes were compiled from KEGG CRC pathway and through text mining as CRC-related gene candidates, while 385 other genes were randomly selected as the non-CRC gene candidates. All these 462 genes were encoded according to their Gene Ontology annotation, each producing a 2669-dimensional vector which was drastically reduced to 52 dimensions after feature selection. A rule set including 7 criteria was revealed by our method, yielding an overall prediction accuracy of 0.9242 and MCC of 0.7259. And analysis of the rule set and optimal features may shed some light on how CRC genes can be separated from non-CRC genes based on GO terms.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer worldwide and mostly occurs in viral hepatitis endemic areas such as China. Knowledge of HCC-related genes may lead to an early detection of HCC and develop molecularly targeted therapeutics, reducing mortality and improving a patient’s prognosis significantly. Therefore, it is valuable and important for us to identify common characters of HCC related genes. In this study, we proposed a computational method to predict HCC related genes based on Gene Ontology terms and KEGG terms using Random Forest (RF), in which features were optimized by maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). 224 HCC gene candidates were compiled from some databases, while 11,200non-HCC gene candidates were randomly selected from Ensemble database. 10 candidate datasets were constructed by dividing non-HCC gene candidates into 10 groups. Each gene in datasets was encoded by 13,126 features including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 615 GO terms and 11 KEGG pathways was discovered. Through analysis, we found these features were closely related to HCC, which means our method is effective for discovering HCC related genes, and it is hopeful that it can also be used to predict and analyze genes for other types of cancer.

Drosophila circadian rhythm is an interesting topic for researchers and a lot of mathematic models have been constructed. Most of the studies of circadian rhythm are based upon the interactive positive and negative feedback loops controlling the circadian clock. Recently many studies have shown that microRNAs (miRNAs) have important effects on the circadian clock. In this paper, we present a detailed mathematic model of miRNA-mediated Drosophila circadian clock system, with incorporation miRNA-regulation, targeted to two different mRNAs per and clk, into an existing model. Based on numerical simulations, we explore the dynamic properties of the circadian clock regulated by miRNAs with different target mRNAs with the help of parameter variation and sensitivity analysis. The results indicate that miRNAs targeted to different mRNAs have different effects on the dynamic behaviors of Drosophila circadian rhythms. This study may help biologists to identify target mRNAs for miRNAs in Drosophila circadian rhythm.

Recently several experiments have shown that REST is bound to the chromatin of miR-21, and miR-21 inhibits the self-renewal of mouse embryonic stem (ES) cells by decreasing the expression of SOX2, NANOG and OCT4. Thus, REST is believed to be a core transcription factor together with miR-21 to regulate differentiation and self-renewal of ES cells. In this paper, a mathematical model is established for investigating the roles of the REST-miR-21 regulatory pathway in the core regulatory network of ES cells. Results show that REST plays a significant role of differentiation and self-renewal by blocking the expression of miR-21, and the changes of expression of REST affects the expression of miR-21, correspondingly leads to the bistable switching curve shift and the length of the bistable region changes, which accordingly makes more opportunities of ES cells in the state of differentiation or self-renewal. Furthermore, results show that the over-expression of REST or miR-21 will lead to an irreversible bistable switch. With appropriate combination of two input external signals of embryonic stem cells, a more robust bistable switch can be obtained. In addition, a model of proliferation cell population is given to explain the relationship between abnormal expression levels of REST (or miR-21) and abnormal proliferation cell population, which will lead to a series of diseases, such as tumors.

A computational systems biology representation was utilized for understanding the molecular and metabolic behaviors of heavy metal assimilation system in the genome Methanosarcina barkeri str. Fusaro. A known functional protein of this system was identified by text mining. A combined functional annotation approach was employed to discover the missing proteins with unknown function. Herein, assimilation systems of cadmium, nickel and copper ions have been predicted by using bioinformatics resources. Metabolic flux balances of each pathway model have been optimized for its cellular behaviors in response to excessive heavy metal ions in the environment. Many heavy metals have been utilized by a typical assimilation system on producing the precursors required for methane biosynthesis, and other energy driven processes of this genome. Metal transporter and accessory proteins of methanogens were phylogenetically corresponded with similar proteins in the members of proteobacteria, but metal-containing enzymes are very exclusive to closely related methanogenic archaea. The evolutionary and metabolic relationships of heavy metal transporter system have been observed among archaea and some members in the proteobacteria. Thus, the metabolic models obtained from this study will optimistically be useful in understanding the heavy metal assimilation mechanism of this genome for producing methane, and applying in bioremediation process.

In an effort to accelerate translational bioinformatics research, this study presents a universal software tool, named as Sequence Relation Drawing program (SRD). It can be used to dynamically visualize the relationship between molecular sequences and their categories based on undirected graphs in similarity analysis of gene and protein sequences. SRD consists of two components: a Window-based application and a computerized database. Researchers can import their datasets into the database, which will make the software run faster and occupy less memory. Pre-computed sequence relations and other user-defined information can also be imported into the system, and then be visualized in several interactive perspectives. Sequences could be partitioned into several categories, and several windows are provided and linked for the visualization involving intro-category, extra-category and category-category relationships, respectively. An example is also provided, which is HIV Pestiferous Map Analysis. Given the sequences of the envelope glycoprotein gene and their similarities, SRD could help to investigate traits of the spread of the AIDS disease, which may help biologists or clinicians to control the AIDS disease transmission in molecular epidemiology study. The SRD software can be download from http://www.nkbiox.com/srd/index.htm.

Intracellular Ca2+ transients have been shown to be induced by ultrasound in various types of cells and Ca2+ plays an important role in cell recovery after sonoporation. To achieve a complete understanding of Ca2+ dynamics during insonation and get clues for suitable parameters of ultrasound to accelerate its clinical application, a new model of ultrasound-induced Ca2+ dynamics has been developed. In the model, effects of ultrasound stimulation on calcium influx and mobilization have been numerically investigated with an assumed linear relation between the low-level ultrasound intensity and induced membrane strain density. The modeling results reproduced the characteristics of elevated intracellular Ca2+ transients induced by ultrasound, showing a biphasic response of intracellular [Ca2+] for about 3 minutes. Numerical results suggested that ultrasound intensity should be between 40 and 1200 mW/cm2 to induce recoverable Ca2+ transients. Stimulation above this intensity range may cause cell damage. This range of intensity changes with cell types.

GO (Gene Ontology) analysis is a technology which shows the enrichment and distribution of genes based on Gene Ontology databases. It has been widely used in the researches on target genes of biological compounds and medicines. However, the associations among target genes, as well as the associations among biological processes, molecular functions and cellular components were not cleared by the enrichment and distribution of target genes in GO(Gene Ontology) analysis. In this study, a new mining algorithm with complex network is given to solve the two problems by the analysis of target genes of anti-aging traditional Chinese medicines. All the data of effective ingredients and target genes for Chinese medicines in this study has been obtained from the databases: HIT, CNPD and TTD. The results show that, the synergistic processes for the target genes of antiaging traditional Chinese medicines may include: “response to stress - response to stimulus - response to chemical stimulus”, “immune system process - positive regulation of immune system process - regulation of immune system process”, and “regulation of apoptosis - negative regulation of apoptosis - regulation of programmed cell death”. The target genes which may play important roles in anti-aging include: NOS2A, PTGS2, CASP3, NFKB3, TNFA, SOD1, IL1B, and BCL2. The target genes with high association and synergies may include: “NOS2A-SOD1”, “BCL2-BAX”, “CASP3-NOS2A”, and “CASP3-NFkB3”.

Annonaceous acetogenins (Annonin-I, IV, VI, VIII &XVI) were tested for their insecticidal property against the dusky cotton bug Oxycarenus laetus Kirby. Bioinformatics approach using PASS indicated Annonin-I to have the highest phosphatase inhibition and toxic activity. The structure of the target (ND1) was modeled using the Robetta server. The Molecular docking of the ligands to the active site of ND1 was carried out using Glide module of Schrodinger and the QikProp program was used to obtain the ADME properties of the analogues. The results indicated that Annonin-I inhibited the enzymes NADH and cytochrome oxidase; which were further validated by in-vitro studies using the lygaeid pest Oxycarenus laetus.

This paper proposes an efficient method to solve the DNA fragment assembly problem using Adaptive Particle Swarm Optimization (APSO). The DNA fragment assembly for shotgun sequencing has been under study with great significance and complexity. It refers to the arrangement of the fragments in an accurate sequence. This fragment assembly problem is an NP-hard combinatorial optimization problem. In this paper, three different methods namely Constant Inertia Weight (CIW), Dynamically Varying Inertia Weight (DVIW) and An Adaptive Particle Swarm Optimization (APSO) with Smallest Position Value (SPV) rule are proposed to solve the DNA fragment assembly problem. The objective of the proposed method is to obtain the maximum overlapping score by assembling the fragments. Particle swarm optimization algorithm is used to analyze the impact of inertia weight, the cognitive and social components. The PSO algorithm was simulated for each of the methods individually. The experimental results are obvious that the proposed APSO method yields better overlap score when tested with different sized benchmark instances. The proposed APSO method is effective and efficient in assembling the fragments and getting the maximum overlap score when compared to other heuristic techniques.

Lacking of diagnostic biomarker for early diagnosis leads to the poor survival rate of lung squamous cell carcinoma (LUSC). Nowadays, development of high throughput technologies provides a critical timing for identifying molecular biomarkers by integrating multifactorial data. In this work, we have integrated the survival data and multifactorial data (transcription factors, microRNAs and gene ontology terms) to analyze the underling progression mechanism of the LUSC and attempt to identify the novel survival-associated biomarkers. We found 298 candidate survival-associated genes correlated with patient survival data using univariate Cox proportional hazards regression model. These survival-associated genes have been significantly regulated by 18 transcription factors and 20 microRNAs, enriched within 19 gene ontology terms. Integrating these information, we identified five survival-associated genes (BAX, BCL6, APP, IL10, BBC3) simultaneously correlation with LUSC survival data, indicating novel biomarkers for earlier detection of LUSC.

To facilitate the similarities/dissimilarities analysis of the protein sequences, we introduce a novel approach based on the recurrence quantification analysis (RQA), in which, based on a selected pair of physicochemical properties of amino acids, the primary structure of proteins is considered as two time series, with the amino acid order playing the role of subsequent time intervals, and then, we adopt RQA to analyze these two time series, and utilize 6 characteristic parameters calculated with RQA as feature representation of protein sequence to analyze the similarities/dissimilarities of the nine ND5 protein sequences. The analysis results show that our method is effective, and in addition, different from existing RQA based methods, in our method, after the two parameters such as the embedding dimension m and the time delay Τ have been predetermined based on given algorithms, the range of the threshold ε can be determined efficiently, and more interesting, the variation of ε in the determined range almost will not influence the rationality of the results of the similarities/dissimilarities analysis.