Combined analysis of chromosomal instabilities and gene expression for colon cancer progression inference

Background

The human body plays host to a vast array of bacteria, found in oral cavities, skin, gastrointestinal tract and the vagina. Some bacteria are harmful while others are beneficial to the host. Despite the availability of many methods to identify bacteria, most of them are only applicable to specific and cultivable bacteria and are also tedious. Based on high throughput sequencing technology, this work derives 16S rRNA sequences of bacteria and analyzes probiotics and pathogens species.

Results

We constructed a database that recorded the species of probiotics and pathogens from literature, along with a modified Smith-Waterman algorithm for assigning the taxonomy of the sequenced 16S rRNA sequences. We also constructed a bacteria disease risk model for seven diseases based on 98 samples. Applicability of the proposed platform is demonstrated by collecting the microbiome in human gut of 13 samples.

Conclusions

The proposed platform provides a relatively easy means of identifying a certain amount of bacteria and their species (including uncultivable pathogens) for clinical microbiology applications. That is, detecting how probiotics and pathogens inhabit humans and how affect their health can significantly contribute to develop a diagnosis and treatment method.     

Use of molecular beacons to verify that the serine hydroxymethyltransferase pseudogene SHMT-ps1 is unique to the order Primates

Background

The serine hydroxymethyltransferase processed pseudogene SHMT-ps1 has been suggested to be unique to the order Primates because of the failure to amplify this sequence by PCR from genomic DNAs of any non-primate mammal species. Here, 'molecular beacon' probes specific to SHMT-ps1 were used in an attempt to verify this suggestion.

Results

In a search for SHMT-ps1-specific sequences using molecular beacons across a range of mammalian species, SHMT-ps1 was only found in primates. The molecular beacon assays also showed that SHMT-ps1 is present in both Old World and New World species but not among prosimians.

Conclusions

These results suggest that SHMT-ps1 originated close to the origin of the Anthropoidea, some 40 to 50 million years ago.     

Discovering joint associations between disease and gene pairs with a novel similarity test

Background

Diverse plant and animal species have B chromosomes, also known as accessory, extra or supernumerary chromosomes. Despite being widely distributed among different taxa, the genomic nature and genetic behavior of B chromosomes are still poorly understood.

Results

In this study we describe the occurrence of B chromosomes in the African cichlid fish Haplochromis obliquidens. One or two large B chromosome(s) occurring in 39.6% of the analyzed individuals (both male and female) were identified. To better characterize the karyotype and assess the nature of the B chromosomes, fluorescence in situ hybridization (FISH) was performed using probes for telomeric DNA repeats, 18S and 5S rRNA genes, SATA centromeric satellites, and bacterial artificial chromosomes (BACs) enriched in repeated DNA sequences. The B chromosomes are enriched in repeated DNAs, especially non-active 18S rRNA gene-like sequences.

Conclusion

Our results suggest that the B chromosome could have originated from rDNA bearing subtelo/acrocentric A chromosomes through formation of an isochromosome, or by accumulation of repeated DNAs and rRNA gene-like sequences in a small proto-B chromosome derived from the A complement.     

Association between fatty acid compositions and genotypes of FABP4 and LXR-alpha in Japanese Black cattle

Background

Single nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common type of polymorphisms and are frequently used for molecular marker development. Such markers have become very popular for all kinds of genetic analysis, including haplotype reconstruction. Haplotypes can be reconstructed for whole chromosomes but also for specific genes, based on the SNPs present. Haplotypes in the latter context represent the different alleles of a gene. The computational approach to SNP mining is becoming increasingly popular because of the continuously increasing number of sequences deposited in databases, which allows a more accurate identification of SNPs. Several software packages have been developed for SNP mining from databases. From these, QualitySNP is the only tool that combines SNP detection with the reconstruction of alleles, which results in a lower number of false positive SNPs and also works much faster than other programs. We have build a web-based SNP discovery and allele detection tool (HaploSNPer) based on QualitySNP.

Results

HaploSNPer is a flexible web-based tool for detecting SNPs and alleles in user-specified input sequences from both diploid and polyploid species. It includes BLAST for finding homologous sequences in public EST databases, CAP3 or PHRAP for aligning them, and QualitySNP for discovering reliable allelic sequences and SNPs. All possible and reliable alleles are detected by a mathematical algorithm using potential SNP information. Reliable SNPs are then identified based on the reconstructed alleles and on sequence redundancy.

Conclusion

Thorough testing of HaploSNPer (and the underlying QualitySNP algorithm) has shown that EST information alone is sufficient for the identification of alleles and that reliable SNPs can be found efficiently. Furthermore, HaploSNPer supplies a user friendly interface for visualization of SNP and alleles. HaploSNPer is available from http://www.bioinformatics.nl/tools/haplosnper/.     

Real-time PCR TaqMan assay for rapid screening of bloodstream infection

Background

Sepsis is one of the main causes of mortality and morbidity. The rapid detection of pathogens in blood of septic patients is essential for adequate antimicrobial therapy and better prognosis. This study aimed to accelerate the detection and discrimination of Gram-positive (GP) and Gram-negative (GN) bacteria and Candida species in blood culture samples by molecular methods.

Methods

The Real-GP®, -GN®, and -CAN® real-time PCR kit (M&D, Wonju, Republic of Korea) assays use the TaqMan probes for detecting pan-GP, pan-GN, and pan-Candida species, respectively. The diagnostic performances of the real-time PCR kits were evaluated with 115 clinical isolates, 256 positive and 200 negative blood culture bottle samples, and the data were compared to results obtained from conventional blood culture.

Results

Eighty-seven reference strains and 115 clinical isolates were correctly identified with specific probes corresponding to GP-bacteria, GN-bacteria and Candida, respectively. The overall sensitivity and specificity of the real-time PCR kit with blood culture samples were 99.6% and 89.5%, respectively.

Conclusions

The Real-GP®, -GN®, and -CAN® real-time PCR kits could be useful tools for the rapid and accurate screening of bloodstream infections (BSIs).     

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

Background

We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm.

Results

We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.

Conclusions

The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.     

Analyzes of genome-wide association follow-up study for calving traits in dairy cattle

Background

Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP).

Results

CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at http://bio.kuas.edu.tw/CpGPAP/.

Conclusions

The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.     

Computable visually observed phenotype ontological framework for plants

Background

Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes.

Results

This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.

Conclusions

A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.     

Distribution of bacterial endophytes in peanut seeds obtained from axenic and control plant material under field conditions

Background and Aims

The role and linkage of endophytic bacteria to resistance of peanut seeds to biotic stress is poorly understood. The aims of the present study were to survey the experimental (axenic) and control (conventional) peanut plants for the predominant endophytic bacteria, and to characterize isolates with activity against selected A. flavus strains.

Methods

Young axenic plants were grown from presumably bacteria-free embryos in the lab, and then they were grown in a field. Endophytic bacterial species were identified by the analysis of DNA sequences of their 16S-ribosomal RNA gene. DNA extracted from soil was also analyzed for predominant bacteria.

Results

Mature seeds from the experimental and control plants contained several species of nonpathogenic endophytic bacteria. Among the eight bacterial species isolated from seeds, and DNA sequences detected in soil, Bacillus thuringiensis was dominant. All B. amyloliquefaciens isolates, the second abundant species in seeds demonstrated activity against A. flavus. This effect was not observed with any other bacterial isolates. There was no significant difference in number and relative occurrence of the two major bacterial species between the experimental and conventionally grown control seeds.

Conclusion

Endophytic bacterial colonization derives from local soil and not from the seed source, and the peanut plant accommodates only selected species of bacteria from diverse soil populations. Some bacterial isolates showed antibiosis against A. flavus.     

HAPSIMU: a genetic simulation platform for population-based association studies

Background

The availability of sequences from whole genomes to reconstruct the tree of life has the potential to enable the development of phylogenomic hypotheses in ways that have not been before possible. A significant bottleneck in the analysis of genomic-scale views of the tree of life is the time required for manual curation of genomic data into multi-gene phylogenetic matrices.

Results

To keep pace with the exponentially growing volume of molecular data in the genomic era, we have developed an automated technique, ASAP (Automated Simultaneous Analysis Phylogenetics), to assemble these multigene/multi species matrices and to evaluate the significance of individual genes within the context of a given phylogenetic hypothesis.

Conclusion

Applications of ASAP may enable scientists to re-evaluate species relationships and to develop new phylogenomic hypotheses based on genome-scale data.     

Efficient algorithms for the discovery of gapped factors

Background

With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters.

Methods

In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot.

Results

Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity.

Conclusions

We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.     

Match-Only Integral Distribution (MOID) Algorithm for high-density oligonucleotide array analysis

Background

High-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics.

Results

A new algorithm, Match-Only Integral Distribution (MOID), was developed to analyze high-density oligonucleotide arrays. Using known data from both spiking experiments and no-change experiments performed with Affymetrix GeneChip^® arrays, MOID and the Affymetrix algorithm implemented in Microarray Suite 4.0 (MAS4) were compared. While MOID gave similar performance to MAS4 in the spiking experiments, better performance was observed in the no-change experiments. MOID also provides a set of alternative statistical analysis tools to MAS4. There are two main features that distinguish MOID from MAS4. First, MOID uses continuous P values for the likelihood of gene presence, while MAS4 resorts to discrete absolute calls. Secondly, MOID uses heuristic confidence intervals for both gene expression levels and fold change values, while MAS4 categorizes the significance of gene expression level changes into discrete fold change calls.

Conclusions

The results show that by using MOID, Affymetrix GeneChip^® arrays may need as little as ten probes per gene without compromising analysis accuracy.     

Multiscale Hy3S: Hybrid stochastic simulation for supercomputers

Background

DNA copy number alterations are one of the main characteristics of the cancer cell karyotype and can contribute to the complex phenotype of these cells. These alterations can lead to gains in cellular oncogenes as well as losses in tumor suppressor genes and can span small intervals as well as involve entire chromosomes. The ability to accurately detect these changes is central to understanding how they impact the biology of the cell.

Results

We describe a novel algorithm called CARAT (Copy Number Analysis with Regression And Tree) that uses probe intensity information to infer copy number in an allele-specific manner from high density DNA oligonuceotide arrays designed to genotype over 100, 000 SNPs. Total and allele-specific copy number estimations using CARAT are independently evaluated for a subset of SNPs using quantitative PCR and allelic TaqMan reactions with several human breast cancer cell lines. The sensitivity and specificity of the algorithm are characterized using DNA samples containing differing numbers of X chromosomes as well as a test set of normal individuals. Results from the algorithm show a high degree of agreement with results from independent verification methods.

Conclusion

Overall, CARAT automatically detects regions with copy number variations and assigns a significance score to each alteration as well as generating allele-specific output. When coupled with SNP genotype calls from the same array, CARAT provides additional detail into the structure of genome wide alterations that can contribute to allelic imbalance.     

A microsatellite linkage map of Drosophila mojavensis

Background

Drosophila mojavensishas been a model system for genetic studies of ecological adaptation and speciation. However, despite its use for over half a century, no linkage map has been produced for this species or its close relatives.

Results

We have developed and mapped 90 microsatellites in D. mojavensis, and we present a detailed recombinational linkage map of 34 of these microsatellites. A slight excess of repetitive sequence was observed on the X-chromosome relative to the autosomes, and the linkage groups have a greater recombinational length than the homologous D. melanogaster chromosome arms. We also confirmed the conservation of Muller's elements in 23 sequences between D. melanogaster and D. mojavensis.

Conclusions

The microsatellite primer sequences and localizations are presented here and made available to the public. This map will facilitate future quantitative trait locus mapping studies of phenotypes involved in adaptation or reproductive isolation using this species.     

A supervised visual model for finding regions of interest in basal cell carcinoma images

Background

Expectation maximizing (EM) is one of the common approaches for image segmentation.

Methods

an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.

Results

the findings show that the proposed algorithm produces higher similarity index.

Conclusions

experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.     

Maximum likelihood models and algorithms for gene tree evolution with duplications and losses

Background

The abundance of new genomic data provides the opportunity to map the location of gene duplication and loss events on a species phylogeny. The first methods for mapping gene duplications and losses were based on a parsimony criterion, finding the mapping that minimizes the number of duplication and loss events. Probabilistic modeling of gene duplication and loss is relatively new and has largely focused on birth-death processes.

Results

We introduce a new maximum likelihood model that estimates the speciation and gene duplication and loss events in a gene tree within a species tree with branch lengths. We also provide an, in practice, efficient algorithm that computes optimal evolutionary scenarios for this model. We implemented the algorithm in the program DrML and verified its performance with empirical and simulated data.

Conclusions

In test data sets, DrML finds optimal gene duplication and loss scenarios within minutes, even when the gene trees contain sequences from several hundred species. In many cases, these optimal scenarios differ from the lca-mapping that results from a parsimony gene tree reconciliation. Thus, DrML provides a new, practical statistical framework on which to study gene duplication.