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Bioinformatics & Computational Biology

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https://www.readbyqxmd.com/read/28417000/hackseq-catalyzing-collaboration-between-biological-and-computational-scientists-via-hackathon
#1
(no author information available yet)
hackseq ( http://www.hackseq.com) was a genomics hackathon with the aim of bringing together a diverse set of biological and computational scientists to work on collaborative bioinformatics projects. In October 2016, 66 participants from nine nations came together for three days for hackseq and collaborated on nine projects ranging from data visualization to algorithm development. The response from participants was overwhelmingly positive with 100% (n = 54) of survey respondents saying they would like to participate in future hackathons...
2017: F1000Research
https://www.readbyqxmd.com/read/28407038/emmaw-computing-minimal-absent-words-in-external-memory
#2
Alice Heliou, Solon P Pissis, Simon J Puglisi
Motivation: The biological significance of minimal absent words has been investigated in genomes of organisms from all domains of life. For instance, three minimal absent words of the human genome were found in Ebola virus genomes (Silva et al., Bioinf., 2015). There exists an O(n)-time and O(n)-space algorithm for computing all minimal absent words of a sequence of length n on a fixed-sized alphabet based on suffix arrays (Barton et al., BMC Bioinf., 2014). A standard implementation of this algorithm, when applied to a large sequence of length n, requires more than 20n bytes of RAM...
April 12, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28398502/pygold-a-python-based-api-for-docking-based-virtual-screening-workflow-generation
#3
Hitesh Patel, Tobias Brinkjost, Oliver Koch
Motivation: Molecular docking is one of the successful approaches in structure based discovery and development of bioactive molecules in chemical biology and medicinal chemistry. Due to the huge amount of computational time that is still required, docking is often the last step in a virtual screening approach. Such screenings are set as workflows spanned over many steps, each aiming at different filtering task. These workflows can be automatized in large parts using python based toolkits except for docking using the docking software GOLD...
April 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28398465/machine-learning-in-computational-biology-to-accelerate-high-throughput-protein-expression
#4
Anand Sastry, Jonathan Monk, Hanna Tegel, Mathias Uhlén, Bernhard O Palsson, Johan Rockberg, Elizabeth Brunk
Motivation: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40,000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecularlevel properties influencing expression and solubility...
April 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28391206/multiple-swarm-ensembles-improving-the-predictive-power-and-robustness-of-predictive-models-and-its-use-in-computational-biology
#5
Pedro Alves, Shuang Liu, Daifeng Wang, Mark Gerstein
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness...
April 5, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28386709/in-silico-analysis-of-five-missense-mutations-in-cyp1b1-gene-in-pakistani-families-affected-with-primary-congenital-glaucoma
#6
Sabika Firasat, Haiba Kaul, Usman Ali Ashfaq, Sobia Idrees
PURPOSE: The purpose of this study was to characterize the five missense mutations in CYP1B1 gene identified in Pakistani families affected with primary congenital glaucoma (PCG) using various bioinformatics and protein modeling tools. METHODS: We previously reported four novel missense mutations in CYP1B1 gene segregating in consanguineous Pakistani families. These mutations were identified by direct sequencing of all coding exons, the exon-intron boundaries and the 5' untranslated region of CYP1B1 using genomic DNA from affected and unaffected family members...
April 6, 2017: International Ophthalmology
https://www.readbyqxmd.com/read/28384412/second-era-of-omics-in-caries-research-moving-past-the-phase-of-disillusionment
#7
M M Nascimento, E Zaura, A Mira, N Takahashi, J M Ten Cate
Novel approaches using OMICS techniques enable a collective assessment of multiple related biological units, including genes, gene expression, proteins, and metabolites. In the past decade, next-generation sequencing ( NGS) technologies were improved by longer sequence reads and the development of genome databases and user-friendly pipelines for data analysis, all accessible at lower cost. This has generated an outburst of high-throughput data. The application of OMICS has provided more depth to existing hypotheses as well as new insights in the etiology of dental caries...
April 1, 2017: Journal of Dental Research
https://www.readbyqxmd.com/read/28361684/nearender-an-r-package-for-functional-interpretation-of-omics-data-via-network-enrichment-analysis
#8
Ashwini Jeggari, Andrey Alexeyenko
BACKGROUND: The statistical evaluation of pathway enrichment, i.e. of gene profiles' confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores...
March 23, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28361683/comparative-network-stratification-analysis-for-identifying-functional-interpretable-network-biomarkers
#9
Chuanchao Zhang, Juan Liu, Qianqian Shi, Tao Zeng, Luonan Chen
BACKGROUND: A major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation...
March 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28347269/the-autophagy-interaction-network-of-the-aging-model-podospora-anserina
#10
Oliver Philipp, Andrea Hamann, Heinz D Osiewacz, Ina Koch
BACKGROUND: Autophagy is a conserved molecular pathway involved in the degradation and recycling of cellular components. It is active either as response to starvation or molecular damage. Evidence is emerging that autophagy plays a key role in the degradation of damaged cellular components and thereby affects aging and lifespan control. In earlier studies, it was found that autophagy in the aging model Podospora anserina acts as a longevity assurance mechanism. However, only little is known about the individual components controlling autophagy in this aging model...
March 27, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28335739/identification-of-long-non-coding-transcripts-with-feature-selection-a-comparative-study
#11
Giovanna M M Ventola, Teresa M R Noviello, Salvatore D'Aniello, Antonietta Spagnuolo, Michele Ceccarelli, Luigi Cerulo
BACKGROUND: The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of sequence-based features have been proposed to distinguish between coding and non-coding transcripts. Among them, open reading frame, conservation scores, nucleotide arrangements, and RNA secondary structure have been used with success in literature to recognize intergenic long non-coding RNAs, a particular subclass of non-coding RNAs...
March 23, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28335718/rnav-2-0-a-visualization-tool-for-bacterial-srna-mediated-regulatory-networks-mining
#12
Romain Bourqui, Isabelle Dutour, Jonathan Dubois, William Benchimol, Patricia Thébault
BACKGROUND: Bacterial sRNA-mediated regulatory networks has been introduced as a powerful way to analyze the fast rewiring capabilities of a bacteria in response to changing environmental conditions. The identification of mRNA targets of bacterial sRNAs is essential to investigate their functional activities. However, this step remains challenging with the lack of knowledge of the topological and biological constraints behind the formation of sRNA-mRNA duplexes. Even with the most sophisticated bioinformatics target prediction tools, the large proportion of false predictions may be prohibitive for further analyses...
March 23, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28334396/quantifying-circular-rna-expression-from-rna-seq-data-using-model-based-framework
#13
Musheng Li, Xueying Xie, Jing Zhou, Mengying Sheng, Xiaofeng Yin, Eun-A Ko, Tong Zhou, Wanjun Gu
Motivation: Circular RNAs (circRNAs) are a class of non-coding RNAs that are widely expressed in various cell lines and tissues of many organisms. Although the exact function of many circRNAs is largely unknown, the cell type- and tissue-specific circRNA expression has implicated their crucial functions in many biological processes. Hence, the quantification of circRNA expression from high-throughput RNA-seq data is becoming important to ascertain. Although many model-based methods have been developed to quantify linear RNA expression from RNA-seq data, these methods are not applicable to circRNA quantification...
March 8, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28334340/meta-analytic-framework-for-liquid-association
#14
Lin Wang, Silvia Liu, Ying Ding, Shin-Sheng Yuan, Yen-Yi Ho, George C Tseng
Motivation: Although coexpression analysis via pair-wise expression correlation is popularly used to elucidate gene-gene interactions at the whole-genome scale, many complicated multi-gene regulations require more advanced detection methods. Liquid association is a powerful tool to detect the dynamic correlation of two gene variables depending on the expression level of a third variable (LA scouting gene). Liquid association detection from single transcriptomic study, however, is often unstable and not generalizable due to cohort bias, biological variation, and limited sample size...
March 11, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28334224/computational-modeling-of-in-vivo-and-in-vitro-protein-dna-interactions-by-multiple-instance-learning
#15
Zhen Gao, Jianhua Ruan
Motivation: The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance...
March 1, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28334215/systematic-inference-of-functional-phosphorylation-events-in-yeast-metabolism
#16
Yu Chen, Yonghong Wang, Jens Nielsen
Motivation: Protein phosphorylation is a post-translational modification that affects proteins by changing their structure and conformation in a rapid and reversible way, and it is an important mechanism for metabolic regulation in cells. Phosphoproteomics enables high-throughput identification of phosphorylation events on metabolic enzymes, but identifying functional phosphorylation events still requires more detailed biochemical characterization. Therefore, development of computational methods for investigating unknown functions of a large number of phosphorylation events identified by phosphoproteomics has received increased attention...
March 2, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28334199/stepwise-inference-of-likely-dynamic-flux-distributions-from-metabolic-time-series-data
#17
Mojdeh Faraji, Eberhard O Voit
Motivation: Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution...
March 7, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28334115/faster-a-user-friendly-tool-for-ultrafast-and-robust-cell-segmentation-in-large-scale-microscopy
#18
Oliver Hilsenbeck, Michael Schwarzfischer, Dirk Loeffler, Sotiris Dimopoulos, Simon Hastreiter, Carsten Marr, Fabian J Theis, Timm Schroeder
Motivation: Quantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.g., high cell densities, cell-to-cell variability and low signal-to-noise ratios. Results: Here, we evaluate accuracy and speed of various state-of-the-art approaches for cell segmentation in light microscopy images using challenging real and synthetic image data...
February 22, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28334094/funnel-gsea-functional-elastic-net-regression-in-time-course-gene-set-enrichment-analysis
#19
Yun Zhang, David J Topham, Juilee Thakar, Xing Qiu
Motivation: Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing...
February 21, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28302551/role-of-structural-bioinformatics-in-drug-discovery-by-computational-snp-analysis-a-proposed-protocol-for-analyzing-variation-at-the-protein-level
#20
REVIEW
David K Brown, Özlem Tastan Bishop
With the completion of the human genome project at the beginning of the 21st century, the biological sciences entered an unprecedented age of data generation, and made its first steps toward an era of personalized medicine. This abundance of sequence data has led to the proliferation of numerous sequence-based techniques for associating variation with disease, such as genome-wide association studies and candidate gene association studies. However, these statistical methods do not provide an understanding of the functional effects of variation...
March 13, 2017: Global Heart
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