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Bioinformatics python

Xiao He, Lukas Folkman, Karsten Borgwardt
Motivation: Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (1) medical records only contain the response of a patient to very few drugs, (2) drugs are recommended by doctors based on their expert judgment, and (3) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs...
March 8, 2018: Bioinformatics
Zhen Chen, Pei Zhao, Fuyi Li, André Leier, Tatiana T Marquez-Lago, Yanan Wang, Geoffrey I Webb, A Ian Smith, Roger J Daly, Kuo-Chen Chou, Jiangning Song
Summary: Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors...
March 8, 2018: Bioinformatics
Balázs Szalkai, Vince Grolmusz
Summary: Artificial intelligence (AI) tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for the high-quality classification of novel, un-categorized data in numerous areas, including biological sequence analysis. Here we introduce SECLAF, a webserver that uses deep neural networks for hierarchical biological sequence classification...
February 27, 2018: Bioinformatics
Clément Viricel, Simon de Givry, Thomas Schiex, Sophie Barbe
Motivation: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces...
February 20, 2018: Bioinformatics
Jasmin Cevost, Cédric Vaillant, Sam Meyer, Burkhard Rost
Motivation: Many DNA-binding proteins recognize their target sequences indirectly, by sensing DNA's response to mechanical distortion. ThreaDNA estimates this response based on high-resolution structures of the protein-DNA complex of interest. Implementing an efficient nanoscale modeling of DNA deformations involving essentially no adjustable parameters, it returns the profile of deformation energy along whole genomes, at base-pair resolution, within minutes on usual laptop/desktop computers...
February 15, 2018: Bioinformatics
Nathan Mih, Elizabeth Brunk, Ke Chen, Edward Catoiu, Anand Sastry, Erol Kavvas, Jonathan M Monk, Zhen Zhang, Bernhard O Palsson, Alfonso Valencia
Summary: Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genomescale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows...
February 12, 2018: Bioinformatics
Petros Kolovos, Rutger W W Brouwer, Christel E M Kockx, Michael Lesnussa, Nick Kepper, Jessica Zuin, A M Ali Imam, Harmen J G van de Werken, Kerstin S Wendt, Tobias A Knoch, Wilfred F J van IJcken, Frank Grosveld
Chromosome conformation capture (3C) and its derivatives (e.g., 4C, 5C and Hi-C) are used to analyze the 3D organization of genomes. We recently developed targeted chromatin capture (T2C), an inexpensive method for studying the 3D organization of genomes, interactomes and structural changes associated with gene regulation, the cell cycle, and cell survival and development. Here, we present the protocol for T2C based on capture, describing all experimental steps and bio-informatic tools in full detail. T2C offers high resolution, a large dynamic interaction frequency range and a high signal-to-noise ratio...
March 2018: Nature Protocols
Dinesh Kumar Barupal, Sili Fan, Oliver Fiehn
Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design...
February 2, 2018: Current Opinion in Biotechnology
Thierry D G A Mondeel, Frédéric Crémazy, Matteo Barberis
Motivation: Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results: We present GEMMER (GEnome-wide tool for Multi-scale Modelling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae...
February 1, 2018: Bioinformatics
Thomas A Darde, Pierre Gaudriault, Rémi Beranger, Clément Lancien, Annaëlle Caillarec-Joly, Olivier Sallou, Nathalie Bonvallot, Cécile Chevrier, Séverine Mazaud-Guittot, Bernard Jégou, Olivier Collin, Emmanuelle Becker, Antoine D Rolland, Frédéric Chalmel
Motivation: At the same time that toxicologists express increasing concern about reproducibility in this field, the development of dedicated databases has already smoothed the path toward improving the storage and exchange of raw toxicogenomic data. Nevertheless, none provides access to analyzed and interpreted data as originally reported in scientific publications. Given the increasing demand for access to this information, we developed TOXsIgN, a repository for TOXicogenomic sIgNatures...
January 27, 2018: Bioinformatics
Luca Pinello, Rick Farouni, Guo-Cheng Yuan
Motivation: With the increasing amount of genomic and epigenomic data in the public domain, a pressing challenge is to integrate these data to investigate the role of epigenetic mechanisms in regulating gene expression and maintenance of cell-identity. To this end, we have implemented a computational pipeline to systematically study epigenetic variability and uncover regulatory DNA sequences. Results: Haystack is a bioinformatics pipeline to identify hotspots of epigenetic variability across different cell-types, cell-type specific cis-regulatory elements, and associated transcription factors...
January 17, 2018: Bioinformatics
Jakob Wirbel, Pedro Cutillas, Julio Saez-Rodriguez
Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment...
2018: Methods in Molecular Biology
Naoko Iida, Yoshihiro Okuda, Osamu Ogasawara, Satoshi Yamashita, Hideyuki Takeshima, Toshikazu Ushijima
AIM: Bioinformatics analysis for Illumina Infinium Human DNA methylation BeadArray is essential, but still remains difficult task for many experimental researchers. We here aimed to develop a browser-accessible bioinformatics tool for analyzing the BeadArray data. MATERIALS & METHODS: The tool was established as an analytical pipeline using R, Perl and Python programming languages. RESULTS: We introduced a method that groups neighboring probes into a genomic block, which facilitated efficient identification of densely methylated/unmethylated regions...
January 18, 2018: Epigenomics
Juhua Zhang, Wenbo Peng, Lei Wang
Motivation: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets of genome-wide nucleosome occupancy profiles and computational tools to the analysis on these data, the complex language of eukaryotic Nucleosome positioning remains incompletely understood. Results: Here, we address this challenge using an approach based on a state-of-the-art machine learning method...
January 10, 2018: Bioinformatics
Hamza Khan, Hamid Mohamadi, Benjamin P Vandervalk, Rene L Warren, Justin Chu, Inanc Birol
Motivation: Sequencing studies on non-model organisms often interrogate both genomes and transcriptomes with massive amounts of short sequences. Such studies require de novo analysis tools and techniques, when the species and closely related species lack high quality reference resources. For certain applications such as de novo annotation, information on putative exons and alternative splicing may be desirable. Results: Here we present ChopStitch, a new method for finding putative exons de novo and constructing splice graphs using an assembled transcriptome and whole genome shotgun sequencing (WGSS) data...
December 29, 2017: Bioinformatics
Leander Dony, Jonas Mackerodt, Scott Ward, Sarah Filippi, Michael P H Stumpf, Juliane Liepe
Motivation: Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. Results: PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology...
December 7, 2017: Bioinformatics
Kristi Gdanetz, Gian Maria Niccolò Benucci, Natalie Vande Pol, Gregory Bonito
BACKGROUND: One of the most crucial steps in high-throughput sequence-based microbiome studies is the taxonomic assignment of sequences belonging to operational taxonomic units (OTUs). Without taxonomic classification, functional and biological information of microbial communities cannot be inferred or interpreted. The internal transcribed spacer (ITS) region of the ribosomal DNA is the conventional marker region for fungal community studies. While bioinformatics pipelines that cluster reads into OTUs have received much attention in the literature, less attention has been given to the taxonomic classification of these sequences, upon which biological inference is dependent...
December 6, 2017: BMC Bioinformatics
John M Gaspar, Ronald P Hart
BACKGROUND: DNA methylation is an epigenetic modification that is studied at a single-base resolution with bisulfite treatment followed by high-throughput sequencing. After alignment of the sequence reads to a reference genome, methylation counts are analyzed to determine genomic regions that are differentially methylated between two or more biological conditions. Even though a variety of software packages is available for different aspects of the bioinformatics analysis, they often produce results that are biased or require excessive computational requirements...
November 29, 2017: BMC Bioinformatics
Daniel Probst, Jean-Louis Reymond
Motivation: During the past decade, big data has become a major tool in scientific endeavors. While statistical methods and algorithms are well-suited for analyzing and summarizing enormous amounts of data, the results do not allow for a visual inspection of the entire data. Current scientific software, including R packages and Python libraries such as ggplot2, matplotlib, and, do not support interactive visualizations of datasets exceeding 100,000 data points on the web. Other solutions enable the web-based visualization of big data only through data reduction or statistical representations...
November 24, 2017: Bioinformatics
Philipp N Spahn, Tyler Bath, Ryan J Weiss, Jihoon Kim, Jeffrey D Esko, Nathan E Lewis, Olivier Harismendy
Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service...
November 20, 2017: Scientific Reports
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