217 research outputs found
Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines
No full textThe Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects
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Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods.
No full textAs genes tend to be co-regulated as gene modules, feature selection in machine learning (ML) on gene expression data can be challenged by the complexity of gene regulation. Here, we present a protocol for reconciling differences in classifier features identified using different ML approaches. We describe steps for loading the PathwaySpace R package, preparing input for analysis, and creating density plots of gene sets. We then detail procedures for testing whether apparently distinct feature sets are related in pathway space. For complete details on the use and execution of this protocol, please refer to Ellrott et al.1
Protein Threading for Genome-Scale Structural Analysis
No full textProtein structure prediction is a necessary tool in the field of bioinformatic analysis. It is a non-trivial process that can add a great deal of information to a genome annotation. This dissertation deals with protein structure prediction through the technique of protein fold recognition and outlines several strategies for the improvement of protein threading techniques. In order to improve protein threading performance, this dissertation begins with an outline of sequence/structure alignment energy functions. A technique called Violated Inequality Minimization is used to quickly adapt to the changing energy landscape as new energy functions are added. To continue the improvement of alignment accuracy and fold recognition, new formulations of energy functions are used for the creation of the sequence/structure alignment. These energies include a formulation of a gap penalty which is dependent on sequence characteristics different from the traditional constant penalty. Another proposed energy is dependent on conserved structural patterns found during threading. These structural patterns have been employed to refine the sequence/structure alignment in my research. The section on Linear Programming Algorithm for protein structure alignment deals with the optimization of an alignment using additional residue-pair energy functions. In the original version of the model, all cores had to be aligned to the target sequence. Our research outlines an expansion of the original threading model which allows for a more flexible alignment by allowing core deletions. Aside from improvements in fold recognition and alignment accuracy, there is also a need to ensure that these techniques can scale for the computational demands of genome level structure prediction. A heuristic decision making processes has been designed to automate the classification and preparation of proteins for prediction. A graph analysis has been applied to the integration of different tools involved in the pipeline. Analysis of the data dependency graph allows for automatic parallelization of genome structure prediction. These different contributions help to improve the overall performance of protein threading and help distribute computations across a large set of computers to help make genome scale protein structure prediction practically feasible
Identifying transcription factor binding sites through Markov chain optimization
No full textAbstract
Even though every cell in an organism contains the same genetic material, each cell does not express the same cohort of genes. Therefore, one of the major problems facing genomic research today is to determine not only which genes are differentially expressed and under what conditions, but also how the expression of those genes is regulated. The first step in determining differential gene expression is the binding of sequence-specific DNA binding proteins (i.e. transcription factors) to regulatory regions of the genes (i.e. promoters and enhancers). An important aspect to understanding how a given transcription factor functions is to know the entire gamut of binding sites and subsequently potential target genes that the factor may bind/regulate. In this study, we have developed a computer algorithm to scan genomic databases for transcription factor binding sites, based on a novel Markov chain optimization method, and used it to scan the human genome for sites that bind to hepatocyte nuclear factor 4 α (HNF4α). A list of 71 known HNF4α binding sites from the literature were used to train our Markov chain model. By looking at the window of 600 nucleotides around the transcription start site of each confirmed gene on the human genome, we identified 849 sites with varying binding potential and experimentally tested 109 of those sites for binding to HNF4α. Our results show that the program was very successful in identifying 77 new HNF4α binding sites with varying binding affinities (i.e. a 71% success rate). Therefore, this computational method for searching genomic databases for potential transcription factor binding sites is a powerful tool for investigating mechanisms of differential gene regulation.
Contact: [email protected]</jats:p
IMPROVEMENT IN PROTEIN SEQUENCE-STRUCTURE ALIGNMENT USING INSERTION/DELETION FREQUENCY ARRAYS
No full textRestriction Enzyme Recognition Sequence Search Program
No full textA critical and difficult part of characterizing restriction enzymes and methylases is the identification of recognition sequences. To simplify this process, we have developed a plasmid transformation method along with a computer program named RM search that determines the exact recognition sequences for given restriction and modification systems
Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods
No full textSummary: As genes tend to be co-regulated as gene modules, feature selection in machine learning (ML) on gene expression data can be challenged by the complexity of gene regulation. Here, we present a protocol for reconciling differences in classifier features identified using different ML approaches. We describe steps for loading the PathwaySpace R package, preparing input for analysis, and creating density plots of gene sets. We then detail procedures for testing whether apparently distinct feature sets are related in pathway space.For complete details on the use and execution of this protocol, please refer to Ellrott et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics
Expansion of the protein repertoire in newly explored environments: human gut microbiome specific protein families.
No full textThe microbes that inhabit particular environments must be able to perform molecular functions that provide them with a competitive advantage to thrive in those environments. As most molecular functions are performed by proteins and are conserved between related proteins, we can expect that organisms successful in a given environmental niche would contain protein families that are specific for functions that are important in that environment. For instance, the human gut is rich in polysaccharides from the diet or secreted by the host, and is dominated by Bacteroides, whose genomes contain highly expanded repertoire of protein families involved in carbohydrate metabolism. To identify other protein families that are specific to this environment, we investigated the distribution of protein families in the currently available human gut genomic and metagenomic data. Using an automated procedure, we identified a group of protein families strongly overrepresented in the human gut. These not only include many families described previously but also, interestingly, a large group of previously unrecognized protein families, which suggests that we still have much to discover about this environment. The identification and analysis of these families could provide us with new information about an environment critical to our health and well being
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