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Bioinformatics python

Kathrin M Seibt, Thomas Schmidt, Tony Heitkam
Summary: FlexiDot is a cross-platform dotplot suite generating high quality self, pairwise and all-against-all visualizations. To improve dotplot suitability for comparison of consensus and error-prone sequences, FlexiDot harbors routines for strict and relaxed handling of ambiguities and substitutions. Our shading modules facilitate dotplot interpretation and motif identification by adding information on sequence annotations and sequence similarities. Combined with collage-like outputs, FlexiDot supports simultaneous visual screening of large sequence sets, enabling dotplot use for routine analyses...
May 14, 2018: Bioinformatics
R Biczok, P Bozsoky, P Eisenmann, J Ernst, T Ribizel, F Scholz, A Trefzer, F Weber, M Hamann, A Stamatakis
Motivation: The presence of terraces in phylogenetic tree space, that is, a potentially large number of distinct tree topologies that have exactly the same analytical likelihood score, was first described by Sanderson et al. (2011). However, popular software tools for maximum likelihood and Bayesian phylogenetic inference do not yet routinely report, if inferred phylogenies reside on a terrace, or not. We believe, this is due to the lack of an efficient library to (i) determine if a tree resides on a terrace, (ii) calculate how many trees reside on a terrace, and (iii) enumerate all trees on a terrace...
May 8, 2018: Bioinformatics
Waqasuddin Khan, Ganapathi Varma- Saripella, Thomas Ludwig, Tania Cuppens, Florian Thibord, Emmanuelle Génin, Jean-Francois Deleuze, David-Alexandre Trégouët
Summary: Predicted deleteriousness of coding variants is a frequently used criterion to filter out variants detected in next-generation sequencing projects and to select candidates impacting on the risk of human diseases. Most available dedicated tools implement a base-to-base annotation approach that could be biased in presence of several variants in the same genetic codon. We here proposed the MACARON program that, from a standard VCF file, identifies, re-annotates and predicts the amino acid change resulting from multiple single nucleotide variants (SNVs) within the same genetic codon...
May 3, 2018: Bioinformatics
Hui Peng, Yi Zheng, Michael Blumenstein, Dacheng Tao, Jinyan Li
Motivation: CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single guide RNAs (sgRNAs) such that its cleavage efficiency is high meanwhile the off-target effect is low. Results: This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM)...
April 16, 2018: Bioinformatics
Amanda Birmingham, Adam M Mark, Carlo Mazzaferro, Guorong Xu, Kathleen M Fisch
Summary: With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package VAPr (Variant Analysis and Prioritization). VAPr leverages existing annotation tools ANNOVAR and with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies...
April 6, 2018: Bioinformatics
Stefan Budach, Annalisa Marsico
Summary: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment...
April 6, 2018: Bioinformatics
Timothy Dallman, Philip Ashton, Ulf Schafer, Aleksey Jironkin, Anais Painset, Sharif Shaaban, Hassan Hartman, Richard Myers, Anthony Underwood, Claire Jenkins, Kathie Grant
Summary: We announce the release of SnapperDB v1.0 a program for scalable routine SNP analysis and storage of microbial populations. Availability: SnapperDB is implemented as a python application under the open source BSD license. All code and user guides are available at Reference genomes and SnapperDB configs are available at Contact: tim...
April 5, 2018: Bioinformatics
Mahabubur Rahman, Hiroshi Watabe
Molecular imaging serves as an important tool for researchers and clinicians to visualize and investigate complex biochemical phenomena using specialized instruments; these instruments are either used individually or in combination with targeted imaging agents to obtain images related to specific diseases with high sensitivity, specificity, and signal-to-noise ratios. However, molecular imaging, which is a multidisciplinary research field, faces several challenges, including the integration of imaging informatics with bioinformatics and medical informatics, requirement of reliable and robust image analysis algorithms, effective quality control of imaging facilities, and those related to individualized disease mapping, data sharing, software architecture, and knowledge management...
April 4, 2018: Computers in Biology and Medicine
Zhenjia Wang, Mete Civelek, Clint L Miller, Nathan C Sheffield, Michael J Guertin, Chongzhi Zang
Summary: Identification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive to detect factors binding at distal enhancers. Here we present Binding Analysis for Regulation of Transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6,000 existing ChIP-seq datasets for over 400 factors in human or mouse...
March 28, 2018: Bioinformatics
Abhishek Kumar
Herein I review recently published book Python for Bioinformatics 2nd Edition. By Sebastian Bassi, Chapman & Hall/CRC Mathematical and Computational Biology, 2017. ISBN: 978-1138035263. With technological advancements, 2nd edition of this book is an excellent upgrade from first edition with several new implementations such as python 3, NoSQL, Anoconda, Git (version control management) and bottle (framework for web development). It is very well written with concise and clear code examples. The detailed explanations of python codes are provided...
March 26, 2018: Proteomics
Stephen J Fletcher, Mikael Boden, Neena Mitter, Bernard J Carroll
Summary: Small RNAs play key roles in gene regulation, defense against viral pathogens and maintenance of genome stability, though many aspects of their biogenesis and function remain to be elucidated. SCRAM (Small Complementary RNA Mapper) is a novel, simple-to-use short read aligner and visualization suite that enhances exploration of small RNA datasets. Availability: The SCRAM pipeline is implemented in Go and Python, and is freely available under MIT license...
March 15, 2018: Bioinformatics
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
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