bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping small molecule binding data to structural domains
Authors: Kruger Felix A , Rostom Raghd , Overington John P
Published: December 2019

Background

Large-scale bioactivity/SAR Open Data has recently become available, and this has allowed new analyses and approaches to be developed to help address the productivity and translational gaps of current drug discovery. One of the current limitations of these data is the relative sparsity of reported interactions per protein target, and complexities in establishing clear relationships between bioactivity and targets using bioinformatics tools. We detail in this paper the indexing of targets by the structural domains that bind (or are likely to bind) the ligand within a full-length protein. Specifically, we present a simple heuristic to map small molecule binding to Pfam domains. This profiling can be applied to all proteins within a genome to give some indications of the potential pharmacological modulation and regulation of all proteins.

Results

In this implementation of our heuristic, ligand binding to protein targets from the ChEMBL database was mapped to structural domains as defined by profiles contained within the Pfam-A database. Our mapping suggests that the majority of assay targets within the current version of the ChEMBL database bind ligands through a small number of highly prevalent domains, and conversely the majority of Pfam domains sampled by our data play no currently established role in ligand binding. Validation studies, carried out firstly against Uniprot entries with expert binding-site annotation and secondly against entries in the wwPDB repository of crystallographic protein structures, demonstrate that our simple heuristic maps ligand binding to the correct domain in about 90 percent of all assessed cases. Using the mappings obtained with our heuristic, we have assembled ligand sets associated with each Pfam domain.

Conclusions

Small molecule binding has been mapped to Pfam-A domains of protein targets in the ChEMBL bioactivity database. The result of this mapping is an enriched annotation of small molecule bioactivity data and a grouping of activity classes following the Pfam-A specifications of protein domains. This is valuable for data-focused approaches in drug discovery, for example when extrapolating potential targets of a small molecule with known activity against one or few targets, or in the assessment of a potential target for drug discovery or screening studies.
B-cell epitope prediction through a graph model
Authors: Zhao Liang , Wong Limsoon , Lu Lanyuan , Hoi Steven CH , Li Jinyan
Published: December 2019

Background

Prediction of B-cell epitopes from antigens is useful to understand the immune basis of antibody-antigen recognition, and is helpful in vaccine design and drug development. Tremendous efforts have been devoted to this long-studied problem, however, existing methods have at least two common limitations. One is that they only favor prediction of those epitopes with protrusive conformations, but show poor performance in dealing with planar epitopes. The other limit is that they predict all of the antigenic residues of an antigen as belonging to one single epitope even when multiple non-overlapping epitopes of an antigen exist.

Results

In this paper, we propose to divide an antigen surface graph into subgraphs by using a Markov Clustering algorithm, and then we construct a classifier to distinguish these subgraphs as epitope or non-epitope subgraphs. This classifier is then taken to predict epitopes for a test antigen. On a big data set comprising 92 antigen-antibody PDB complexes, our method significantly outperforms the state-of-the-art epitope prediction methods, achieving 24.7% higher averaged f-score than the best existing models. In particular, our method can successfully identify those epitopes with a non-planarity which is too small to be addressed by the other models. Our method can also detect multiple epitopes whenever they exist.

Conclusions

Various protrusive and planar patches at the surface of antigens can be distinguishable by using graphical models combined with unsupervised clustering and supervised learning ideas. The difficult problem of identifying multiple epitopes from an antigen can be made easied by using our subgraph approach. The outstanding residue combinations found in the supervised learning will be useful for us to form new hypothesis in future studies.
Discovery of a new class of inhibitors for the protein arginine deiminase type 4 (PAD4) by structure-based virtual screening
Authors: Teo Chian Ying , Shave Steven , Thean Chor Adam Leow , Salleh Abu Bakar , Abdul Rahman Mohd Basyaruddin Bin , Walkinshaw Malcolm D , Tejo Bimo A
Published: December 2019

Background

Rheumatoid arthritis (RA) is an autoimmune disease with unknown etiology. Anticitrullinated protein autoantibody has been documented as a highly specific autoantibody associated with RA. Protein arginine deiminase type 4 (PAD4) is the enzyme responsible for catalyzing the conversion of peptidylarginine into peptidylcitrulline. PAD4 is a new therapeutic target for RA treatment. In order to search for inhibitors of PAD4, structure-based virtual screening was performed using LIDAEUS (Ligand discovery at Edinburgh university). Potential inhibitors were screened experimentally by inhibition assays.

Results

Twenty two of the top-ranked water-soluble compounds were selected for inhibitory screening against PAD4. Three compounds showed significant inhibition of PAD4 and their IC50 values were investigated. The structures of the three compounds show no resemblance with previously discovered PAD4 inhibitors, nor with existing drugs for RA treatment.

Conclusion

Three compounds were discovered as potential inhibitors of PAD4 by virtual screening. The compounds are commercially available and can be used as scaffolds to design more potent inhibitors against PAD4.
An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds
Authors: Nakamura Masaomi , Hachiya Tsuyoshi , Saito Yutaka , Sato Kengo , Sakakibara Yasubumi
Published: December 2019

Background

Prediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored.

Results

We formulated pathway prediction between a start compound and a goal compound as the shortest path search problem in terms of the number of enzyme reactions applied. We propose an efficient search method based on A* algorithm and heuristic techniques utilizing Linear Programming (LP) solution for estimation of the distance to the goal. First, a chemical compound is represented by a feature vector which counts frequencies of substructure occurrences in the structural formula. Second, an enzyme reaction is represented as an operator vector by detecting the structural changes to compounds before and after the reaction. By defining compound vectors as nodes and operator vectors as edges, prediction of the reaction pathway is reduced to the shortest path search problem in the vector space. In experiments on the DDT degradation pathway, we verify that the shortest paths predicted by our method are biologically correct pathways registered in the KEGG database. The results also demonstrate that the LP heuristics can achieve significant reduction in computation time. Furthermore, we apply our method to a secondary metabolite pathway of plant origin, and successfully find a novel biochemical pathway which cannot be predicted by the existing method. For the reconstruction of a known biochemical pathway, our method is over 40 times as fast as the existing method.

Conclusions

Our method enables fast and accurate de novo pathway predictions and novel pathway detection.
ARG-based genome-wide analysis of cacao cultivars
Authors: Utro Filippo , Cornejo Omar Eduardo , Livingstone Donald , Motamayor Juan Carlos , Parida Laxmi
Published: December 2019

Background

Ancestral recombinations graph (ARG) is a topological structure that captures the relationship between the extant genomic sequences in terms of genetic events including recombinations. IRiS is a system that estimates the ARG on sequences of individuals, at genomic scales, capturing the relationship between these individuals of the species. Recently, this system was used to estimate the ARG of the recombining X Chromosome of a collection of human populations using relatively dense, bi-allelic SNP data.

Results

While the ARG is a natural model for capturing the inter-relationship between a single chromosome of the individuals of a species, it is not immediately apparent how the model can utilize whole-genome (across chromosomes) diploid data. Also, the sheer complexity of an ARG structure presents a challenge to graph visualization techniques. In this paper we examine the ARG reconstruction for (1) genome-wide or multiple chromosomes, (2) multi-allelic and (3) extremely sparse data. To aid in the visualization of the results of the reconstructed ARG, we additionally construct a much simplified topology, a classification tree, suggested by the ARG.

As the test case, we study the problem of extracting the relationship between populations of Theobroma cacao. The chocolate tree is an outcrossing species in the wild, due to self-incompatibility mechanisms at play. Thus a principled approach to understanding the inter-relationships between the different populations must take the shuffling of the genomic segments into account. The polymorphisms in the test data are short tandem repeats (STR) and are multi-allelic (sometimes as high as 30 distinct possible values at a locus). Each is at a genomic location that is bilaterally transmitted, hence the ARG is a natural model for this data. Another characteristic of this plant data set is that while it is genome-wide, across 10 linkage groups or chromosomes, it is very sparse, i.e., only 96 loci from a genome of approximately 400 megabases. The results are visualized both as MDS plots and as classification trees. To evaluate the accuracy of the ARG approach, we compare the results with those available in literature.

Conclusions

We have extended the ARG model to incorporate genome-wide (ensemble of multiple chromosomes) data in a natural way. We present a simple scheme to implement this in practice. Finally, this is the first time that a plant population data set is being studied by estimating its underlying ARG. We demonstrate an overall precision of 0.92 and an overall recall of 0.93 of the ARG-based classification, with respect to the gold standard. While we have corroborated the classification of the samples with that in literature, this opens the door to other potential studies that can be made on the ARG.
A flexible ancestral genome reconstruction method based on gapped adjacencies
Authors: Gagnon Yves , Blanchette Mathieu , El-Mabrouk Nadia
Published: December 2019

Background

The "small phylogeny" problem consists in inferring ancestral genomes associated with each internal node of a phylogenetic tree of a set of extant species. Existing methods can be grouped into two main categories: the distance-based methods aiming at minimizing a total branch length, and the synteny-based (or mapping) methods that first predict a collection of relations between ancestral markers in term of "synteny", and then assemble this collection into a set of Contiguous Ancestral Regions (CARs). The predicted CARs are likely to be more reliable as they are more directly deduced from observed conservations in extant species. However the challenge is to end up with a completely assembled genome.

Results

We develop a new synteny-based method that is flexible enough to handle a model of evolution involving whole genome duplication events, in addition to rearrangements, gene insertions, and losses. Ancestral relationships between markers are defined in term of Gapped Adjacencies, i.e. pairs of markers separated by up to a given number of markers. It improves on a previous restricted to direct adjacencies, which revealed a high accuracy for adjacency prediction, but with the drawback of being overly conservative, i.e. of generating a large number of CARs. Applying our algorithm on various simulated data sets reveals good performance as we usually end up with a completely assembled genome, while keeping a low error rate.

Availability

All source code is available at http://www.iro.umontreal.ca/~mabrouk.
Modeling the interactions of Alzheimer-related genes from the whole brain microarray data and diffusion tensor images of human brain
Authors: Park Byungkyu , Lee Wook , Han Kyungsook
Published: December 2019

Background

In recent years the genome-wide microarray-based gene expression profiles and diffusion tensor images (DTI) in human brain have been made available with accompanying anatomic and histology data. The challenge is to integrate various types of data to investigate the interactions of genes that are associated with specific neurological disorder.

Results

In this study, we analyzed the whole brain microarray data and the physical connectivity of the hippocampus with other brain regions to identify the genes related to Alzheimer's disease and their interactions with proteins. We generated a physical connectivity map of the left and right hippocampuses with 12 other brain regions and identified 33 Alzheimer-related genes that interact with many proteins. These genes are highly linked to the development of Alzheimer's disease.

Conclusions

In Alzheimer's brain both brain regions and inter-regional communications through the white matter are often hampered. So far the connectivity of regions in Alzheimer's brain has been studied mostly at the functional level using functional MRI (fMRI). Analyzing the inter-regional fiber connectivity without tracking crossing-fiber regions often provides coarse and inaccurate results. A few deep brain fibers were analyzed but the inter-regional fiber connectivity was not analyzed in their studies. The inter-regional fiber connectivity analysis can provide comprehensive and measurable degradation of fiber tracts in AD patients' brains, but is not easy to perform. We tracked crossing-fiber regions and identified genes with high expression levels in the fiber pathways of the hippocampus. The interactions of the genes with other proteins can provide comprehensive and measurable degradation of fiber tracts in Alzheimer brains. To the best of our knowledge, this is the first attempt to integrate the whole brain microarray data with DTI data to identify specific genes and their interactions.
OmicsVis: an interactive tool for visually analyzing metabolomics data
Authors: Livengood Philip , Maciejewski Ross , Chen Wei , Ebert David S
Published: December 2019

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery.
iStable: off-the-shelf predictor integration for predicting protein stability changes
Authors: Chen Chi-Wei , Lin Jerome , Chu Yen-Wei
Published: December 2019

Background

Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users.

Results

We proposed an integrated predictor, iStable, with grid computing architecture constructed by using sequence information and prediction results from different element predictors. In the learning model, several machine learning methods were evaluated and adopted the support vector machine as an integrator, while not just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available with two different input types: structural and sequential. After training and cross-validation, iStable has better performance than all of the element predictors on several datasets. Under different classifications and conditions for validation, this study has also shown better overall performance in different types of secondary structures, relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental conditions.

Conclusions

The trained and validated version of iStable provides an accurate approach for prediction of protein stability changes. iStable is freely available online at: http://predictor.nchu.edu.tw/iStable.
The regulatory effect of miRNAs is a heritable genetic trait in humans
Authors: Geeleher Paul , Huang Stephanie R , Gamazon Eric R , Golden Aaron , Seoighe Cathal
Published: December 2019

Background

microRNAs (miRNAs) have been shown to regulate the expression of a large number of genes and play key roles in many biological processes. Several previous studies have quantified the inhibitory effect of a miRNA indirectly by considering the expression levels of genes that are predicted to be targeted by the miRNA and this approach has been shown to be robust to the choice of prediction algorithm. Given a gene expression dataset, Cheng et al. defined the regulatory effect score (RE-score) of a miRNA as the difference in the gene expression rank of targets of the miRNA compared to non-targeted genes.

Results

Using microarray data from parent-offspring trios from the International HapMap project, we show that the RE-score of most miRNAs is correlated between parents and offspring and, thus, inter-individual variation in RE-score has a genetic component in humans. Indeed, the mean RE-score across miRNAs is correlated between parents and offspring, suggesting genetic differences in the overall efficiency of the miRNA biogenesis pathway between individuals. To explore the genetics of this quantitative trait further, we carried out a genome-wide association study of the mean RE-score separately in two HapMap populations (CEU and YRI). No genome-wide significant associations were discovered; however, a SNP rs17409624, in an intron of DROSHA, was significantly associated with mean RE-score in the CEU population following permutation-based control for multiple testing based on all SNPs mapped to the canonical miRNA biogenesis pathway; of 244 individual miRNA RE-scores assessed in the CEU, 214 were associated (p < 0.05) with rs17409624. The SNP was also nominally significantly associated (p = 0.04) with mean RE-score in the YRI population. Interestingly, the same SNP was associated with 17 (8.5% of all expressed) miRNA expression levels in the CEU. We also show here that the expression of the targets of most miRNAs is more highly correlated with global changes in miRNA regulatory effect than with the expression of the miRNA itself.

Conclusions

We present evidence that miRNA regulatory effect is a heritable trait in humans and that a polymorphism of the DROSHA gene contributes to the observed inter-individual differences.
Genome-wide search for miRNA-target interactions in Arabidopsis thaliana with an integrated approach
Authors: Ding Jiandong , Li Danqing , Ohler Uwe , Guan Jihong , Zhou Shuigeng
Published: December 2019

Background

MiRNA are about 22nt long small noncoding RNAs that post transcriptionally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated computational programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs.

Methods

Here we present an integrated MTI prediction and analysis toolkit (imiRTP) for Arabidopsis thaliana. It features two important functions: (i) combination of several effective plant miRNA target prediction methods provides a sufficiently large MTI candidate set, and (ii) different filters allow for an efficient selection of potential targets. The modularity of imiRTP enables the prediction of high quality targets on genome-wide scale. Moreover, predicted MTIs can be presented in various ways, which allows for browsing through the putative target sites as well as conducting simple and advanced analyses.

Results

Results show that imiRTP could always find high quality candidates compared with single method by choosing appropriate filter and parameter. And we also reveal that a portion of plant miRNA could bind target genes out of coding region. Based on our results, imiRTP could facilitate the further study of Arabidopsis miRNAs in real use. All materials of imiRTP are freely available under a GNU license at (http://admis.fudan.edu.cn/projects/imiRTP.htm).
Computational discovery and RT-PCR validation of novel Burkholderia conserved and Burkholderia pseudomallei unique sRNAs
Authors: Khoo Jia-Shiun , Chai Shiao-Fei , Mohamed Rahmah , Nathan Sheila , Firdaus-Raih Mohd
Published: December 2019

Background

The sRNAs of bacterial pathogens are known to be involved in various cellular roles including environmental adaptation as well as regulation of virulence and pathogenicity. It is expected that sRNAs may also have similar functions for Burkholderia pseudomallei, a soil bacterium that can adapt to diverse environmental conditions, which causes the disease melioidosis and is also able to infect a wide variety of hosts.

Results

By integrating several proven sRNA prediction programs into a computational pipeline, available Burkholderia spp. genomes were screened to identify sRNA gene candidates. Orthologous sRNA candidates were then identified via comparative analysis. From the total prediction, 21 candidates were found to have Rfam homologs. RT-PCR and sequencing of candidate sRNA genes of unknown functions revealed six putative sRNAs which were highly conserved in Burkholderia spp. and two that were unique to B. pseudomallei present in a normal culture conditions transcriptome. The validated sRNAs include potential cis-acting elements associated with the modulation of methionine metabolism and one B. pseudomallei-specific sRNA that is expected to bind to the Hfq protein.

Conclusions

The use of the pipeline developed in this study and subsequent comparative analysis have successfully aided in the discovery and shortlisting of sRNA gene candidates for validation. This integrated approach identified 29 B. pseudomallei sRNA genes - of which 21 have Rfam homologs and 8 are novel.
Preferential regulation of stably expressed genes in the human genome suggests a widespread expression buffering role of microRNAs
Authors: Yang Zhen , Dong Dong , Zhang Zhaolei , Crabbe M James C , Wang Li , Zhong Yang
Published: December 2019

Background

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate the target gene expression at post-transcriptional level. They are widely involved in biological processes, such as embryonic development, cell division, differentiation, and apoptosis. Evidence suggests that miRNAs can constrain the variation of their target to buffer the fluctuation of expression. However, whether this effect can act on the genome-wide expression remains controversial.

Results

In this study, we comprehensively explored the stably expressed genes (SE genes) and fluctuant genes (FL genes) in the human genome by a meta-analysis of large scale microarray data. We found that these genes have distinct function distributions. miRNA targets are shown to be significantly enriched in SE genes by using propensity analysis of miRNA regulation, supporting the hypothesis that miRNAs can buffer whole genome expression fluctuation. The expression-buffering effect of miRNA is independent of the target site number within the 3'-untranslated region. In addition, we found that gene expression fluctuation is positively correlated with the number of transcription factor binding sites in the promoter region, which suggests that coordination between transcription factors and miRNAs leads to balanced responses to external perturbations.

Conclusions

Our study confirmed that the genetic buffering roles of miRNAs can act on genome expression fluctuation and provides insights into how miRNAs and transcription factors coordinate to cope with external perturbation.
Discovering pathway cross-talks based on functional relations between pathways
Authors: Hsu Chia-Lang , Yang Ueng-Cheng
Published: December 2019

Background

In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations.

Results

This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis.

Conclusions

This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.
A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework
Authors: Chang Yu-Jung , Chen Chien-Chih , Chen Chuen-Liang , Ho Jan-Ming
Published: December 2019

Background

State-of-the-art high-throughput sequencers, e.g., the Illumina HiSeq series, generate sequencing reads that are longer than 150 bp up to a total of 600 Gbp of data per run. The high-throughput sequencers generate lengthier reads with greater sequencing depth than those generated by previous technologies. Two major challenges exist in using the high-throughput technology for de novo assembly of genomes. First, the amount of physical memory may be insufficient to store the data structure of the assembly algorithm, even for high-end multicore processors. Moreover, the graph-theoretical model used to capture intersection relationships of the reads may contain structural defects that are not well managed by existing assembly algorithms.

Results

We developed a distributed genome assembler based on string graphs and MapReduce framework, known as the CloudBrush. The assembler includes a novel edge-adjustment algorithm to detect structural defects by examining the neighboring reads of a specific read for sequencing errors and adjusting the edges of the string graph, if necessary. CloudBrush is evaluated against GAGE benchmarks to compare its assembly quality with the other assemblers. The results show that our assemblies have a moderate N50, a low misassembly rate of misjoins, and indels of > 5 bp. In addition, we have introduced two measures, known as precision and recall, to address the issues of faithfully aligned contigs to target genomes. Compared with the assembly tools used in the GAGE benchmarks, CloudBrush is shown to produce contigs with high precision and recall. We also verified the effectiveness of the edge-adjustment algorithm using simulated datasets and ran CloudBrush on a nematode dataset using a commercial cloud. CloudBrush assembler is available at https://github.com/ice91/CloudBrush.
Quantitative assessment of mitochondrial DNA copies from whole genome sequencing
Authors: Chu Hsueh-Ting , Hsiao William WL , Tsao Theresa TH , Chang Ching-Mao , Liu Yen-Wenn , Fan Chen-Chieh , Lin Han , Chang Hen-Hong , Yeh Tze-Jung , Chen Jen-Chih , Huang Dun-Ming , Chen Chaur-Chin , Kao Cheng-Yan
Published: December 2019

Background

Mitochondrial dysfunction is associated with various aging diseases. The copy number of mtDNA in human cells may therefore be a potential biomarker for diagnostics of aging. Here we propose a new computational method for the accurate assessment of mtDNA copies from whole genome sequencing data.

Results

Two families of the human whole genome sequencing datasets from the HapMap and the 1000 Genomes projects were used for the accurate counting of mitochondrial DNA copy numbers. The results revealed the parental mitochondrial DNA copy numbers are significantly lower than that of their children in these samples. There are 8%~21% more copies of mtDNA in samples from the children than from their parents. The experiment demonstrated the possible correlations between the quantity of mitochondrial DNA and aging-related diseases.

Conclusions

Since the next-generation sequencing technology strives to deliver affordable and non-biased sequencing results, accurate assessment of mtDNA copy numbers can be achieved effectively from the output of whole genome sequencing. We implemented the method as a software package MitoCounter with the source code and user's guide available to the public at http://sourceforge.net/projects/mitocounter/.
BM-Map: an efficient software package for accurately allocating multireads of RNA-sequencing data
Authors: Yuan Yuan , Norris Clift , Xu Yanxun , Tsui Kam-Wah , Ji Yuan , Liang Han
Published: December 2019

Background

RNA sequencing (RNA-seq) has become a major tool for biomedical research. A key step in analyzing RNA-seq data is to infer the origin of short reads in the source genome, and for this purpose, many read alignment/mapping software programs have been developed. Usually, the majority of mappable reads can be mapped to one unambiguous genomic location, and these reads are called unique reads. However, a considerable proportion of mappable reads can be aligned to more than one genomic location with the same or similar fidelities, and they are called "multireads". Allocating these multireads is challenging but critical for interpreting RNA-seq data. We recently developed a Bayesian stochastic model that allocates multireads more accurately than alternative methods (Ji et al. Biometrics 2011).

Results

In order to serve a greater biological community, we have implemented this method in a stand-alone, efficient, and user-friendly software package, BM-Map. BM-Map takes SAM (Sequence Alignment/Map), the most popular read alignment format, as the standard input; then based on the Bayesian model, it calculates mapping probabilities of multireads for competing genomic loci; and BM-Map generates the output by adding mapping probabilities to the original SAM file so that users can easily perform downstream analyses. The program is available in three common operating systems, Linux, Mac and PC. Moreover, we have built a dedicated website, http://bioinformatics.mdanderson.org/main/BM-Map, which includes free downloads, detailed tutorials and illustration examples.

Conclusions

We have developed a stand-alone, efficient, and user-friendly software package for accurately allocating multireads, which is an important addition to our previous methodology paper. We believe that this bioinformatics tool will greatly help RNA-seq and related applications reach their full potential in life science research.
Pathway analysis of genome-wide data improves warfarin dose prediction
Authors: Daneshjou Roxana , Tatonetti Nicholas P , Karczewski Konrad J , Sagreiya Hersh , Bourgeois Stephane , Drozda Katarzyna , Burmester James K , Tsunoda Tatsuhiko , Nakamura Yusuke , Kubo Michiaki , Tector Matthew , Limdi Nita A , Cavallari Larisa H , Perera Minoli , Johnson Julie A , Klein Teri E , Altman Russ B
Published: December 2019

Background

Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.

Results

Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.

Conclusions

Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.
New proteoliposome vaccine formulation from N. meningitidis serogroup B, without aluminum hydroxide, retains its antimeningococcal protectogenic potential as well as Th-1 adjuvant capacity
Authors: Tamargo Beatriz , Márquez Yanet , Ramírez Wendy , Cedré Bárbara , Fresno Manuel , Sierra Gustavo
Published: December 2019

Proteoliposomes purified from the Outer Membrane of Neisseria meningitidis B, have been successfully used as core for adjuvants and vaccine formulations. We have tried to increase their structural definition and to conserve their efficacy and stability avoiding the addition of the aluminum hydroxide to the final formulation. Liposomal particle systems were prepared from components of defined molecular structure, such as a Neisseria meningitidis B protein complex, extracted and purified without forming vesicle structures. Liposomes were prepared from a mixture of dioleoyl phosphatidyl serine and cholesterol, using the classical dehydration-rehydration method. Transmission Electron Microscopy (TEM) was used to characterize the liposomes. BALB/c mice were used for animal testing procedures. Analysis of specific IgG response, serum bactericidal activity as well as DTH reaction was carried out. Isolation and purification of mRNA and real-time PCR, was performed to determine the dominating Th lymphokine pattern. The new antimeningococcal formulation without aluminum hydroxide prepared with components of defined molecular structure assembled itself into Neoproteoliposomes (NPL) ranging from 50 to 70 nm in diameter. The extraction and purification of selected membrane proteins to provide the antigen for this new formulation (PD-Tp), as well as the NPL-formulation favors a Th1 response pattern, suggested by the higher percentages of DTH, increased expression of proinflamatory lymphokine mRNAs when administered by intramuscular and intranasal routes. It stimulates a systemic bactericidal antibody response against Neisseria meningitidis B and immunologic memory similar to the Cuban VA-MENGOC-BC® vaccine, even at lower dosages and is less reactogenic at the injection site in comparison with the formulation with aluminum hydroxide. This new adjuvant formulation could be applicable to the development of new and improved vaccines against meningococcal disease, and eventually as modulators of the immune response against other diseases.
Distinct target cell-dependent forms of short-term plasticity of the central visceral afferent synapses of the rat
Authors: Yamamoto Kiyofumi , Noguchi Jun , Yamada Chiaki , Watabe Ayako M , Kato Fusao
Published: December 2019

Background

The visceral afferents from various cervico-abdominal sensory receptors project to the dorsal vagal complex (DVC), which is composed of the nucleus of the solitary tract (NTS), the area postrema and the dorsal motor nucleus of the vagus nerve (DMX), via the vagus and glossopharyngeal nerves and then the solitary tract (TS) in the brainstem. While the excitatory transmission at the TS-NTS synapses shows strong frequency-dependent suppression in response to repeated stimulation of the afferents, the frequency dependence and short-term plasticity at the TS-DMX synapses, which also transmit monosynaptic information from the visceral afferents to the DVC neurons, remain largely unknown.

Results

Recording of the EPSCs activated by paired or repeated TS stimulation in the brainstem slices of rats revealed that, unlike NTS neurons whose paired-pulse ratio (PPR) is consistently below 0.6, the distribution of the PPR of DMX neurons shows bimodal peaks that are composed of type I (PPR, 0.6-1.5; 53% of 120 neurons recorded) and type II (PPR, < 0.6; 47%) neurons. Some of the type I DMX neurons showed paired-pulse potentiation. The distinction of these two types depended on the presynaptic release probability and the projection target of the postsynaptic cells; the distinction was not dependent on the location or soma size of the cell, intensity or site of the stimulation, the latency, standard deviation of latency or the quantal size. Repeated stimulation at 20 Hz resulted in gradual and potent decreases in EPSC amplitude in the NTS and type II DMX neurons, whereas type I DMX neurons displayed only slight decreases, which indicates that the DMX neurons of this type could be continuously activated by repeated firing of primary afferent fibers at a high (~10 Hz) frequency.

Conclusions

These two general types of short-term plasticity might contribute to the differential activation of distinct vago-vagal reflex circuits, depending on the firing frequency and type of visceral afferents.
The impact of media coverage on the transmission dynamics of human influenza
Authors: Tchuenche Jean M , Dube Nothabo , Bhunu Claver P , Smith Robert J , Bauch Chris T
Published: December 2019

Background

There is an urgent need to understand how the provision of information influences individual risk perception and how this in turn shapes the evolution of epidemics. Individuals are influenced by information in complex and unpredictable ways. Emerging infectious diseases, such as the recent swine flu epidemic, may be particular hotspots for a media-fueled rush to vaccination; conversely, seasonal diseases may receive little media attention, despite their high mortality rate, due to their perceived lack of newness.

Methods

We formulate a deterministic transmission and vaccination model to investigate the effects of media coverage on the transmission dynamics of influenza. The population is subdivided into different classes according to their disease status. The compartmental model includes the effect of media coverage on reporting the number of infections as well as the number of individuals successfully vaccinated.

Results

A threshold parameter (the basic reproductive ratio) is analytically derived and used to discuss the local stability of the disease-free steady state. The impact of costs that can be incurred, which include vaccination, education, implementation and campaigns on media coverage, are also investigated using optimal control theory. A simplified version of the model with pulse vaccination shows that the media can trigger a vaccinating panic if the vaccine is imperfect and simplified messages result in the vaccinated mixing with the infectives without regard to disease risk.

Conclusions

The effects of media on an outbreak are complex. Simplified understandings of disease epidemiology, propogated through media soundbites, may make the disease significantly worse.
Modelling and analysis of influenza A (H1N1) on networks
Authors: Jin Zhen , Zhang Juping , Song Li-Peng , Sun Gui-Quan , Kan Jianli , Zhu Huaiping
Published: December 2019

Background

In April 2009, a new strain of H1N1 influenza virus, referred to as pandemic influenza A (H1N1) was first detected in humans in the United States, followed by an outbreak in the state of Veracruz, Mexico. Soon afterwards, this new virus kept spreading worldwide resulting in a global outbreak. In China, the second Circular of the Ministry of Health pointed out that as of December 31, 2009, the country’s 31 provinces had reported 120,000 confirmed cases of H1N1.

Methods

We formulate an epidemic model of influenza A based on networks. We calculate the basic reproduction number and study the effects of various immunization schemes. The final size relation is derived for the network epidemic model. The model parameters are estimated via least-squares fitting of the model solution to the observed data in China.

Results

For the network model, we prove that the disease-free equilibrium is globally asymptotically stable when the basic reproduction is less than one. The final size will depend on the vaccination starting time, T, the number of infective cases at time T and immunization schemes to follow. Our theoretical results are confirmed by numerical simulations. Using the parameter estimates based on the observation data of the cumulative number of hospital notifications, we estimate the basic reproduction number R
0 to be 1.6809 in China.

Conclusions

Network modelling supplies a useful tool for studying the transmission of H1N1 in China, capturing the main features of the spread of H1N1. While a uniform, mass-immunization strategy helps control the prevalence, a targeted immunization strategy focusing on specific groups with given connectivity may better control the endemic.
On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach
Authors: Lee Junkyu , Kim Seongsoon , Lee Sunwon , Lee Kyubum , Kang Jaewoo
Published: December 2019

Background

Most previous Protein Protein Interaction (PPI) studies evaluated their algorithms' performance based on "per-instance" precision and recall, in which the instances of an interaction relation were evaluated independently. However, we argue that this standard evaluation method should be revisited. In a large corpus, the same relation can be described in various different forms and, in practice, correctly identifying not all but a small subset of them would often suffice to detect the given interaction.

Methods

In this regard, we propose a more pragmatic "per-relation" basis performance evaluation method instead of the conventional per-instance basis method. In the per-relation basis method, only a subset of a relation's instances needs to be correctly identified to make the relation positive. In this work, we also introduce a new high-precision rule-based PPI extraction algorithm. While virtually all current PPI extraction studies focus on improving F-score, aiming to balance the performance on both precision and recall, in many realistic scenarios involving large corpora, one can benefit more from a high-precision algorithm than a high-recall counterpart.

Results

We show that our algorithm not only achieves better per-relation performance than previous solutions but also serves as a good complement to the existing PPI extraction tools. Our algorithm improves the performance of the existing tools through simple pipelining.

Conclusion

The significance of this research can be found in that this research brought new perspective to the performance evaluation of PPI extraction studies, which we believe is more important in practice than existing evaluation criteria. Given the new evaluation perspective, we also showed the importance of a high-precision extraction tool and validated the efficacy of our rule-based system as the high-precision tool candidate.
Toll-like receptor polymorphisms and cerebral malaria: TLR2 Δ22 polymorphism is associated with protection from cerebral malaria in a case control study
Authors: Greene Jennifer A , Sam-Agudu Nadia , John Chandy C , Opoka Robert O , Zimmerman Peter A , Kazura James W
Published: December 2019

Background

In malaria endemic areas, host genetics influence whether aPlasmodium falciparum-infected child develops uncomplicated or severe malaria. TLR2 has been identified as a receptor forP. falciparum-derived glycosylphosphatidylinositol (GPI), and polymorphisms within the TLR2 gene may affect disease pathogenesis. There are two common polymorphisms in the 5' un-translated region (UTR) of TLR2, a 22 base pair deletion in the first unstranslated exon (Δ22), and a GT dinucleotide repeat in the second intron (GTn).

Methods

These polymorphisms were examined in a Ugandan case control study on children with either cerebral malaria or uncomplicated malaria. Serum cytokine levels were analysed by ELISA, according to genotype and disease status. In vitro TLR2 expression was measured according to genotype.

Results

Both Δ22 and GTn polymorphisms were highly frequent, but only Δ22 heterozygosity was associated with protection from cerebral malaria (OR 0.34, 95% confidence intervals 0.16, 0.73). In vitro, heterozygosity for Δ22 was associated with reduced pam3cys inducible TLR2 expression in human monocyte derived macrophages. In uncomplicated malaria patients, Δ22 homozygosity was associated with elevated serum IL-6 (p= 0.04), and long GT repeat alleles were associated with elevated TNF (p= 0.007).

Conclusion

Reduced inducible TLR2 expression may lead to attenuated pro-inflammatory responses, a potential mechanism of protection from cerebral malaria present in individuals heterozygous for the TLR2 Δ22 polymorphism.