Automated processing of NGS data from raw sequencing files to ready-to-use information tables for genome modeling

Epigenetic features such as histone and DNA modifications are important mechanisms for the regulation of gene expression and for cell and tissue development. As a result, extensive efforts are currently undertaken using next-generation sequencing (NGS) to generate vast amounts of data regarding the epigenetic regulation of genomes. Several tools and frameworks for the processing of these NGS data have been developed in the last decade. Nevertheless, each user still bares the challenge to integrate all these tasks to perform the analysis. This procedure is not only tedious but also resource-intensive due to the putative large processing power involved. To automate, standardize and speed up the handling of NGS data, with focus on ChIP-seq data, we present a user-friendly pipeline that automatically processes a list of sequencing data files and returns a ready-to-use purified table for subsequent modelling or analysis attempts.

Dynamics of Associations Between Single Nucleotide Polymorphisms in Relation to Alzheimer's Disease Captured with a New Measure of Linkage Disequilibrium

Genome-Wide-Association-Studies have become a powerful method to link point mutations (e.g. single nucleotide polymorphisms (SNPs)) to a certain phenotype or a disease. However, their power to detect SNPs associated to polygenic diseases such as Alzheimer's Disease (AD) is limited, since they can only infer the pairwise relation of single SNPs to the phenotype and ignore possible effects of various SNP combinations. The common method to probe these possible complex genetic patterns is to compute a measure called linkage disequilibrium (LD). Despite the fact that several predictive patterns found with LD could successfully be applied to medical diagnosis, this measure still holds several drawbacks as for example the difficulty to confirm and replicate experimental results as well as its sensitivity to statistical biases. Here, we present the application of an alternative method, Linkage Probability (LP) for genetic pattern identification that provides the posterior probability of a relation between two categorical data sets and simultaneously considers potential biases from latent variables, such as the recombination rate or the genetic structure of a population. By applying the LP framework to data from the ADSP-Project, we show that changes of linkage patterns between SNPs can be associated to Alzheimer's disease. Common genomic relation measures still fail to extract this link.

Of Wheat and Men: Changes in Genetic Markers for Celiac Disease Over Time

In the recent past, sequencing of ancient human genomes has become increasingly common, leading to an immense amount of data to be explored. For this study we focused on comparing a set of ancient individuals with modern populations on behalf of markers for celiac disease. We analyzed a panel of 64 SNPs related to this disease, trying to detect changes in allele frequencies between ancient and modern individuals. We hope to make a contribution to the subject of genetic health throughout human history.

Decoupling of DNA Methylation Status and Gene Expression Levels in Aging Individuals

Aging is a multi-factorial process, where epigenetic factors play one of the major roles in declines of gene expression and organic function. DNA methylation at CpG islands of promoters can directly change the expression of the neighbouring gene mostly through inhibition. Furthermore, it is known that DNA methylation patterns change during aging In our study, we investigated gene regulation through DNA methylation of genes up- and downregulated in long-lived people compared to a younger cohort. Our data revealed that comparatively highly methylated genes were associated with high expression in long-lived people (e.g. over 85). Genes with lower levels of methylation were associated with low expression. These findings might contradict the general model used to associate methylation status with expression. Indeed, we found that methylation in the promoter regions of all investigated genes is rather constant across different age groups, meaning that the disparity between methylation and expression only happens in older people. A potential explanation could be the impact of other epigenetic mechanisms, possibly related to stress.

Highlights from the International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) – 2015 and 2016 Editions

The International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) annually joint over 200 world-wide scientists in Granada (Spain) to present and discuss their most recent researches in bioinformatics and computational biology. This special issue collects a selection of the most relevant contributions in the two last editions of this conference (IWBBIO 2015 and IWBBIO 2016).

Principal components analysis: theory and application to gene expression data analysis

Advances in computational power have enabled research to generate significant amounts of data related to complex biological problems. Consequently, applying appropriate data analysis techniques has become paramount to tackle this complexity. However, theoretical understanding of statistical methods is necessary to ensure that the correct method is used and that sound inferences are made based on the analysis. In this article, we elaborate on the theory behind principal components analysis (PCA), which has become a favoured multivariate statistical tool in the field of omics-data analysis. We discuss the necessary prerequisites and steps to produce statistically valid results and provide guidelines for interpreting the output. Using PCA on gene expression data from a mouse experiment, we demonstrate that the main distinctive pattern in the data is associated with the transgenic mouse line and is not related to the mouse gender. A weaker association of the pattern with the genotype was also identified.

Identification of Protein Complexes Associated with the Usher Syndrome 2C and Epilepsy-Associated Protein VLGR1 Applying Affinity Proteomics

Authors aimed to identify novel VLGR1-associated protein networks to shed light on its integration into signaling pathways and the cellular compartments in which VLGR1 functions using high-resolution affinity proteomics based on tandem affinity purifications (TAPs).

Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes. In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders. The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current status and limitations of protein interaction data and computational resources. This editorial outlines the meeting's background and programme, putting special emphasis on the extended abstracts of contributed talks collected in the present issue of Genomics and Computational Biology.

Modeling and Simulating Constrained Protein Interaction Networks

Cellular functions of biochemical reactions are enabled by protein interactions. In addition to the protein interactions themselves, dependencies between these interactions such as allosteric activation or mutual exclusion contribute to the complexity and functionality of these systems. We introduce a model of constrained protein interaction networks that uses propositional logic to combine protein networks with interaction dependencies. Further, we present an efficient model, enabling a fast simulation and analysis of many proteins in large networks. This allows to simulate perturbation effects (over-expression/knockout of single or multiple proteins, protein concentrations changes). A comparison of simulation results with known true dependencies against simulated complex formation without dependencies shows that interaction dependencies limit the resulting complex sizes. Further, we show how propagation of perturbation effects is influenced by the interplay of network topology and interaction dependencies and how to analyze this with our model.

Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model

In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts’ advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts’ behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts’ activation.