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Bioinformatics & Computational Biology

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https://www.readbyqxmd.com/read/28209127/repo-an-r-package-for-data-centered-management-of-bioinformatic-pipelines
#1
Francesco Napolitano
BACKGROUND: Reproducibility in Data Analysis research has long been a significant concern, particularly in the areas of Bioinformatics and Computational Biology. Towards the aim of developing reproducible and reusable processes, Data Analysis management tools can help giving structure and coherence to complex data flows. Nonetheless, improved software quality comes at the cost of additional design and planning effort, which may become impractical in rapidly changing development environments...
February 16, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28205527/an-end-to-end-software-solution-for-the-analysis-of-high-throughput-single-cell-migration-data
#2
Paola Masuzzo, Lynn Huyck, Aleksandra Simiczyjew, Christophe Ampe, Lennart Martens, Marleen Van Troys
The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool...
February 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28187425/computational-lipidomics-and-lipid-bioinformatics-filling-in-the-blanks
#3
Josch Pauling, Edda Klipp
Lipids are highly diverse metabolites of pronounced importance in health and disease. While metabolomics is a broad field under the omics umbrella that may also relate to lipids, lipidomics is an emerging field which specializes in the identification, quantification and functional interpretation of complex lipidomes. Today, it is possible to identify and distinguish lipids in a high-resolution, high-throughput manner and simultaneously with a lot of structural detail. However, doing so may produce thousands of mass spectra in a single experiment which has created a high demand for specialized computational support to analyze these spectral libraries...
December 22, 2016: Journal of Integrative Bioinformatics
https://www.readbyqxmd.com/read/28187420/current-progress-of-high-throughput-microrna-differential-expression-analysis-and-random-forest-gene-selection-for-model-and-non-model-systems-an-r-implementation
#4
Jing Zhang, Hanane Hadj-Moussa, Kenneth B Storey
MicroRNAs are short non-coding RNA transcripts that act as master cellular egulators with roles in orchestrating virtually all biological functions. The recent affordability and widespread use of high-throughput microRNA profiling technologies has grown along with the advancement of bioinformatics tools available for analysis of the mounting data flow. While there are many computational resources available for the management of data from genome sequenced animals, researchers are often faced with the challenge of identifying the biological implications of the daunting amount of data generated from these high-throughput technologies...
December 22, 2016: Journal of Integrative Bioinformatics
https://www.readbyqxmd.com/read/28185571/hippi-highly-accurate-protein-family-classification-with-ensembles-of-hmms
#5
Nam-Phuong Nguyen, Michael Nute, Siavash Mirarab, Tandy Warnow
BACKGROUND: Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. RESULTS: We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification)...
November 11, 2016: BMC Genomics
https://www.readbyqxmd.com/read/28182018/genome-wide-base-resolution-mapping-of-dna-methylation-in-single-cells-using-single-cell-bisulfite-sequencing-scbs-seq
#6
Stephen J Clark, Sébastien A Smallwood, Heather J Lee, Felix Krueger, Wolf Reik, Gavin Kelsey
DNA methylation (DNAme) is an important epigenetic mark in diverse species. Our current understanding of DNAme is based on measurements from bulk cell samples, which obscures intercellular differences and prevents analyses of rare cell types. Thus, the ability to measure DNAme in single cells has the potential to make important contributions to the understanding of several key biological processes, such as embryonic development, disease progression and aging. We have recently reported a method for generating genome-wide DNAme maps from single cells, using single-cell bisulfite sequencing (scBS-seq), allowing the quantitative measurement of DNAme at up to 50% of CpG dinucleotides throughout the mouse genome...
March 2017: Nature Protocols
https://www.readbyqxmd.com/read/28152994/transductive-learning-as-an-alternative-to-translation-initiation-site-identification
#7
Cristiano Lacerda Nunes Pinto, Cristiane Neri Nobre, Luis Enrique Zárate
BACKGROUND: The correct protein coding region identification is an important and latent problem in the molecular biology field. This problem becomes a challenge due to the lack of deep knowledge about the biological systems and unfamiliarity of conservative characteristics in the messenger RNA (mRNA). Therefore, it is fundamental to research for computational methods aiming to help the patterns discovery for identification of the Translation Initiation Sites (TIS). In the field of Bioinformatics, machine learning methods have been widely applied based on the inductive inference, as Inductive Support Vector Machine (ISVM)...
February 2, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28150243/ndex-a-community-resource-for-sharing-and-publishing-of-biological-networks
#8
Rudolf T Pillich, Jing Chen, Vladimir Rynkov, David Welker, Dexter Pratt
Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of big data. Research organizations can use NDEx as a distribution channel for networks they generate or curate...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28141874/metabox-a-toolbox-for-metabolomic-data-analysis-interpretation-and-integrative-exploration
#9
Kwanjeera Wanichthanarak, Sili Fan, Dmitry Grapov, Dinesh Kumar Barupal, Oliver Fiehn
Similar to genomic and proteomic platforms, metabolomic data acquisition and analysis is becoming a routine approach for investigating biological systems. However, computational approaches for metabolomic data analysis and integration are still maturing. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. With the number of options provided in each analysis module, it also supports data analysis of other 'omic' families...
2017: PloS One
https://www.readbyqxmd.com/read/28132181/computational-strategies-for-biological-interpretation-of-metabolomics-data
#10
Jianguo Xia
Biological interpretation of metabolomics data relies on two basic steps: metabolite identification and functional analysis. These two steps need to be applied in a coordinated manner to enable effective data understanding. The focus of this chapter is to introduce the main computational concepts and workflows during this process. After a general overview of the field, three sections will be presented: the first section will introduce the main computational methods and bioinformatics tools for metabolite identification using spectra from common analytical platforms; the second section will focus on introducing major bioinformatics approaches for functional enrichment analysis of metabolomics data; and the last section will discuss the three main workflows in current metabolomics studies, including the chemometrics approach, the metabolic profiling approach and the more recent chemo-enrichment analysis approach...
2017: Advances in Experimental Medicine and Biology
https://www.readbyqxmd.com/read/28113952/enumerating-substituted-benzene-isomers-of-tree-like-chemical-graphs
#11
Jinghui Li, Hiroshi Nagamochi, Tatsuya Akutsu
Enumeration of chemical structures is useful for drug design, which is one of the main targets of computational biology and bioinformatics. A chemical graph G with no other cycles than benzene rings is called tree-like, and becomes a tree T possibly with multiple edges if we contract each benzene ring into a single virtual atom of valence 6. All tree-like chemical graphs with a given tree representation T are called the substituted benzene isomers of T. When we replace each virtual atom in T with a benzene ring to obtain a substituted benzene isomer, distinct isomers of T are caused by the difference in arrangements of atom groups around a benzene ring...
November 15, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28109426/wgcna-application-to-proteomic-and-metabolomic-data-analysis
#12
G Pei, L Chen, W Zhang
Progresses in mass spectrometric instrumentation and bioinformatics identification algorithms made over the past decades allow quantitative measurements of relative or absolute protein/metabolite amounts in cells in a high-throughput manner, which has significantly expedited the exploration into functions and dynamics of complex biological systems. However, interpretation of high-throughput data is often restricted by the limited availability of suitable computational methods and enough statistical power. While many computational methodologies have been developed in the past decades to address the issue, it becomes clear that network-focused rather than individual gene/protein-focused strategies would be more appropriate to obtain a complete picture of cellular responses...
2017: Methods in Enzymology
https://www.readbyqxmd.com/read/28109249/parameter-estimation-in-large-scale-systems-biology-models-a-parallel-and-self-adaptive-cooperative-strategy
#13
David R Penas, Patricia González, Jose A Egea, Ramón Doallo, Julio R Banga
BACKGROUND: The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times...
January 21, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28101858/coiled-coil-design-updated-and-upgraded
#14
Derek N Woolfson
α-Helical coiled coils are ubiquitous protein-folding and protein-interaction domains in which two or more α-helical chains come together to form bundles. Through a combination of bioinformatics analysis of many thousands of natural coiled-coil sequences and structures, plus empirical protein engineering and design studies, there is now a deep understanding of the sequence-to-structure relationships for this class of protein architecture. This has led to considerable success in rational design and what might be termed in biro de novo design of simple coiled coils, which include homo- and hetero-meric parallel dimers, trimers and tetramers...
2017: Sub-cellular Biochemistry
https://www.readbyqxmd.com/read/28099457/comparing-and-evaluating-metagenome-assembly-tools-from-a-microbiologist-s-perspective-not-only-size-matters
#15
John Vollmers, Sandra Wiegand, Anne-Kristin Kaster
With the constant improvement in cost-efficiency and quality of Next Generation Sequencing technologies, shotgun-sequencing approaches -such as metagenomics- have nowadays become the methods of choice for studying and classifying microorganisms from various habitats. The production of data has dramatically increased over the past years and processing and analysis steps are becoming more and more of a bottleneck. Limiting factors are partly the availability of computational resources, but mainly the bioinformatics expertise in establishing and applying appropriate processing and analysis pipelines...
2017: PloS One
https://www.readbyqxmd.com/read/28097472/proceedings-of-the-third-international-molecular-pathological-epidemiology-mpe-meeting
#16
REVIEW
Peter T Campbell, Timothy R Rebbeck, Reiko Nishihara, Andrew H Beck, Colin B Begg, Alexei A Bogdanov, Yin Cao, Helen G Coleman, Gordon J Freeman, Yujing J Heng, Curtis Huttenhower, Rafael A Irizarry, N Sertac Kip, Franziska Michor, Daniel Nevo, Ulrike Peters, Amanda I Phipps, Elizabeth M Poole, Zhi Rong Qian, John Quackenbush, Harlan Robins, Peter K Rogan, Martha L Slattery, Stephanie A Smith-Warner, Mingyang Song, Tyler J VanderWeele, Daniel Xia, Emily C Zabor, Xuehong Zhang, Molin Wang, Shuji Ogino
Molecular pathological epidemiology (MPE) is a transdisciplinary and relatively new scientific discipline that integrates theory, methods, and resources from epidemiology, pathology, biostatistics, bioinformatics, and computational biology. The underlying objective of MPE research is to better understand the etiology and progression of complex and heterogeneous human diseases with the goal of informing prevention and treatment efforts in population health and clinical medicine. Although MPE research has been commonly applied to investigating breast, lung, and colorectal cancers, its methodology can be used to study most diseases...
February 2017: Cancer Causes & Control: CCC
https://www.readbyqxmd.com/read/28096084/timesvector-a-vectorized-clustering-approach-to-the-analysis-of-time-series-transcriptome-data-from-multiple-phenotypes
#17
Inuk Jung, Kyuri Jo, Hyejin Kang, Hongryul Ahn, Youngjae Yu, Sun Kim
MOTIVATION: Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i...
January 17, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28077404/high-throughput-sequencing-of-the-t-cell-receptor-repertoire-pitfalls-and-opportunities
#18
James M Heather, Mazlina Ismail, Theres Oakes, Benny Chain
T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information...
January 10, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/28076851/pse-analysis-a-python-package-for-dna-rna-and-protein-peptide-sequence-analysis-based-on-pseudo-components-and-kernel-methods
#19
Bin Liu, Hao Wu, Deyuan Zhang, Xiaolong Wang, Kuo-Chen Chou
To expedite the pace in conducting genome/proteome analysis, we have developed a Python package called Pse-Analysis. The powerful package can automatically complete the following five procedures: (1) sample feature extraction, (2) optimal parameter selection, (3) model training, (4) cross validation, and (5) evaluating prediction quality. All the work a user needs to do is to input a benchmark dataset along with the query biological sequences concerned. Based on the benchmark dataset, Pse-Analysis will automatically construct an ideal predictor, followed by yielding the predicted results for the submitted query samples...
January 5, 2017: Oncotarget
https://www.readbyqxmd.com/read/28069594/sigmod-an-exact-and-efficient-method-to-identify-a-strongly-interconnected-disease-associated-module-in-a-gene-network
#20
Yuanlong Liu, Myriam Brossard, Damian Roqueiro, Patricia Margaritte-Jeannin, Chloé Sarnowski, Emmanuelle Bouzigon, Florence Demenais
MOTIVATION: Apart from single marker-based tests classically used in genome-wide association studies (GWAS), network-assisted analysis has become a promising approach to identify a set of genes associated with disease. To date, most network-assisted methods aim at finding genes connected in a background network, whatever the density or strength of their connections. This can hamper the findings as sparse connections are non-robust against noise from either the GWAS results or the network resource...
January 9, 2017: Bioinformatics
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