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Articles 1 - 13 of 13
Full-Text Articles in Life Sciences
Statistical Potentials For Rna-Protein Interactions Optimized By Cma-Es, Takayuki Kimura, Nobuaki Yasuo, Masakazu Sekijima, Brooke Lustig
Statistical Potentials For Rna-Protein Interactions Optimized By Cma-Es, Takayuki Kimura, Nobuaki Yasuo, Masakazu Sekijima, Brooke Lustig
Faculty Research, Scholarly, and Creative Activity
Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.
Bioinformatics Metadata Extraction For Machine Learning Analysis, Zachary Tom
Bioinformatics Metadata Extraction For Machine Learning Analysis, Zachary Tom
Master's Projects
Next generation sequencing (NGS) has revolutionized the biological sciences. Today, entire genomes can be rapidly sequenced, enabling advancements in personalized medicine, genetic diseases, and more. The National Center for Biotechnology Information (NCBI) hosts the Sequence Read Archive (SRA) containing vast amounts of valuable NGS data. Recently, research has shown that sequencing errors in conventional NGS workflows are key confounding factors for detecting mutations. Various steps such as sample handling and library preparation can introduce artifacts that affect the accuracy of calling rare mutations. Thus, there is a need for more insight into the exact relationship between various steps of the …
Draft Genome Sequences Of Three Monokaryotic Isolates Of The White-Rot Basidiomycete Fungus Dichomitus Squalens, Sara Casado López, Mao Peng, Paul Daly, Bill Andreopoulos, Jasmyn Pangilinan, Anna Lipzen, Robert Riley, Steven Ahrendt, Vivian Ng, Kerrie Barry, Chris Daum, Igor Grigoriev, Kristiina Hildén, Miia Mäkelä, Ronald De Vries
Draft Genome Sequences Of Three Monokaryotic Isolates Of The White-Rot Basidiomycete Fungus Dichomitus Squalens, Sara Casado López, Mao Peng, Paul Daly, Bill Andreopoulos, Jasmyn Pangilinan, Anna Lipzen, Robert Riley, Steven Ahrendt, Vivian Ng, Kerrie Barry, Chris Daum, Igor Grigoriev, Kristiina Hildén, Miia Mäkelä, Ronald De Vries
Faculty Publications, Computer Science
Here, we report the draft genome sequences of three isolates of the wood-decaying white-rot basidiomycete fungus Dichomitus squalens. The genomes of these monokaryons were sequenced to provide more information on the intraspecies genomic diversity of this fungus and were compared to the previously sequenced genome of D. squalens LYAD-421 SS1.
Efficient Unfolding Pattern Recognition In Single Molecule Force Spectroscopy Data, Bill Andreopoulos, Dirk Labudde
Efficient Unfolding Pattern Recognition In Single Molecule Force Spectroscopy Data, Bill Andreopoulos, Dirk Labudde
Faculty Publications, Computer Science
BackgroundSingle-molecule force spectroscopy (SMFS) is a technique that measures the force necessary to unfold a protein. SMFS experiments generate Force-Distance (F-D) curves. A statistical analysis of a set of F-D curves reveals different unfolding pathways. Information on protein structure, conformation, functional states, and inter- and intra-molecular interactions can be derived.ResultsIn the present work, we propose a pattern recognition algorithm and apply our algorithm to datasets from SMFS experiments on the membrane protein bacterioRhodopsin (bR). We discuss the unfolding pathways found in bR, which are characterised by main peaks and side peaks. A main peak is the result of the pairwise …
Rna Secondary Structure Prediction Tool, Meenakshee Mali
Rna Secondary Structure Prediction Tool, Meenakshee Mali
Master's Projects
Ribonucleic Acid (RNA) is one of the major macromolecules essential to all forms of life. Apart from the important role played in protein synthesis, it performs several important functions such as gene regulation, catalyst of biochemical reactions and modification of other RNAs. In some viruses, instead of DNA, RNA serves as the carrier of genetic information. RNA is an interesting subject of research in the scientific community. It has lead to important biological discoveries. One of the major problems researchers are trying to solve is the RNA structure prediction problem. It has been found that the structure of RNA is …
Ab Initio Protein Structure Prediction Algorithms, Maciej Kicinski
Ab Initio Protein Structure Prediction Algorithms, Maciej Kicinski
Master's Projects
Genes that encode novel proteins are constantly being discovered and added to databases, but the speed with which their structures are being determined is not keeping up with this rate of discovery. Currently, homology and threading methods perform the best for protein structure prediction, but they are not appropriate to use for all proteins. Still, the best way to determine a protein's structure is through biological experimentation. This research looks into possible methods and relations that pertain to ab initio protein structure prediction. The study includes the use of positional and transitional probabilities of amino acids obtained from a non-redundant …
A Microrna Target Prediction Algorithm, Rupinder Singh
A Microrna Target Prediction Algorithm, Rupinder Singh
Master's Projects
MicroRNA target prediction using the experimental methods is a challenging task. To accelerate the process of miRNA target validation, many computational methods are used. Computational methods yield many potential candidates for experimental validation. This project is about developing a new computational method using dynamic programming to predict miRNA targets with more accuracy. The project discusses the currently available computational methods and develops a new algorithm using the currently available knowledge about miRNA interactions.
Triangle Network Motifs Predict Complexes By Complementing High-Error Interactomes With Structural Information, Bill Andreopoulos, Christof Winter, Dirk Labudde, Michael Schroeder
Triangle Network Motifs Predict Complexes By Complementing High-Error Interactomes With Structural Information, Bill Andreopoulos, Christof Winter, Dirk Labudde, Michael Schroeder
Faculty Publications, Computer Science
BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from …
Word Sense Disambiguation In Biomedical Ontologies With Term Co-Occurrence Analysis And Document Clustering, Bill Andreopoulos, Dimitra Alexopoulou, Michael Schroeder
Word Sense Disambiguation In Biomedical Ontologies With Term Co-Occurrence Analysis And Document Clustering, Bill Andreopoulos, Dimitra Alexopoulou, Michael Schroeder
Faculty Publications, Computer Science
With more and more genomes being sequenced, a lot of effort is devoted to their annotation with terms from controlled vocabularies such as the GeneOntology. Manual annotation based on relevant literature is tedious, but automation of this process is difficult. One particularly challenging problem is word sense disambiguation. Terms such as |development| can refer to developmental biology or to the more general sense. Here, we present two approaches to address this problem by using term co-occurrences and document clustering. To evaluate our method we defined a corpus of 331 documents on development and developmental biology. Term co-occurrence analysis achieves an …
Unraveling Protein Networks With Power Graph Analysis, Loïc Royer, Matthias Reimann, Bill Andreopoulos, Michael Schroeder
Unraveling Protein Networks With Power Graph Analysis, Loïc Royer, Matthias Reimann, Bill Andreopoulos, Michael Schroeder
Faculty Publications, Computer Science
Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we …
Finding Molecular Complexes Through Multiple Layer Clustering Of Protein Interaction Networks, Bill Andreopoulos, Aijun An, Xiangji Huang, Xiaogang Wang
Finding Molecular Complexes Through Multiple Layer Clustering Of Protein Interaction Networks, Bill Andreopoulos, Aijun An, Xiangji Huang, Xiaogang Wang
Faculty Publications, Computer Science
Clustering protein-protein interaction networks (PINs) helps to identify complexes that guide the cell machinery. Clustering algorithms often create a flat clustering, without considering the layered structure of PINs. We propose the MULIC clustering algorithm that produces layered clusters. We applied MULIC to five PINs. Clusters correlate with known MIPS protein complexes. For example, a cluster of 79 proteins overlaps with a known complex of 88 proteins. Proteins in top cluster layers tend to be more representative of complexes than proteins in bottom layers. Lab work on finding unknown complexes or determining drug effects can be guided by top layer proteins.
Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang
Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang
Faculty Publications, Computer Science
Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for |Bi-Level Clustering of Mixed categorical and numerical data types|. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.
Interview: Brenda Laurel, Jason Challas
Interview: Brenda Laurel, Jason Challas
SWITCH
This interview with Brenda Laurel, Virtual Reality (VR) author and thinker, discusses the applications and challenges of VR. Creating an emphatic experience using VR technology is possible, but the challenge lies in designing an environment that models the senses to stimulate emotions. VR enables experiences of different genders, but physiological differences between the sexes exist and are important to understand. However, technology used to create the environment and simulation of physical objects in VR is only in the developmental stage. Laurel believes in the importance of keeping the mind grounded in the physical body, in order to strengthen the appreciation …