scholarly journals Dynalogo: an interactive sequence logo with dynamic thresholding of matched quantitative proteomic data

2019 ◽  
Vol 36 (5) ◽  
pp. 1632-1633
Author(s):  
Adam T Lafontaine ◽  
Bruce J Mayer ◽  
Kazuya Machida
Keyword(s):  
Dynamic Change ◽  
Sequence Logo ◽  
Supplementary Information ◽  
Threshold Control ◽  
Proteomic Data ◽  
Wide Range ◽  
Modular Domain ◽  
Statistical Programming ◽  
Data Files ◽  
Domain Peptide

Abstract Summary Current web-based sequence logo analyses for studying domain–peptide interactions are often conducted only on high affinity binders due to conservative data thresholding. We have developed Dynalogo, a combination of threshold varying tool and sequence logo generator written in the R statistical programming language, which allows on-the-fly visualization of binding specificity over a wide range of affinity interactions. Hence researchers can easily explore their dataset without the constraint of an arbitrary threshold. After importing quantitative data files, there are various data filtering and visualizing features available. Using a threshold control, users can easily track the dynamic change of enrichment and depletion of amino acid characters in the sequence logo panel. The built-in export function allows downloading filtered data and graphical outputs for further analyses. Dynalogo is optimized for analysis of modular domain–peptide binding experiments but the platform offers a broader application including quantitative proteomics. Availability and implementation Dynalogo application, user manual and sample data files are available at https://dynalogo.cam.uchc.edu. The source code is available at https://github.com/lafontaine-uchc/dynalogo. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Epidemiological modeling in StochSS Live!

2021 ◽  
Author(s):  
Richard Jiang ◽  
Bruno Jacob ◽  
Matthew Geiger ◽  
Sean Matthew ◽  
Bryan Rumsey ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Epidemiological Model ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Based ◽  
Epidemiological Modeling ◽  
Modeling Simulation ◽  
Wide Range ◽  
Biochemical Systems

Abstract Summary We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results. Availability and implementation StochSS Live! is freely available at https://live.stochss.org/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R

2021 ◽  
Author(s):  
Darawan Rinchai ◽  
Jessica Roelands ◽  
Mohammed Toufiq ◽  
Wouter Hendrickx ◽  
Matthew C Altman ◽  
...  
Keyword(s):  
Transcript Abundance ◽  
R Package ◽  
Supplementary Information ◽  
Illustrative Case ◽  
Bioinformatic Tools ◽  
Transcriptional Module ◽  
Wide Range ◽  
Downstream Analysis ◽  
Computing Module ◽  
Parallel Workflow

Abstract Motivation We previously described the construction and characterization of generic and reusable blood transcriptional module repertoires. More recently we released a third iteration (“BloodGen3” module repertoire) that comprises 382 functionally annotated gene sets (modules) and encompasses 14,168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here a R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data

2019 ◽  
Vol 35 (24) ◽  
pp. 5103-5112
Author(s):  
Albert Y Xue ◽  
Angela M Yu ◽  
Julius B Lucks ◽  
Neda Bagheri
Keyword(s):  
Structural Dynamics ◽  
Rna Folding ◽  
Signal Recognition Particle ◽  
Supplementary Information ◽  
Bias Error ◽  
Signal Recognition ◽  
Structural Transitions ◽  
Chemical Probing ◽  
Wide Range ◽  
Nascent Rna

Abstract Motivation RNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution ‘reactivities’ for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation. Results Detection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms. Availability and implementation https://github.com/BagheriLab/DUETT. Supplementary information Supplementary data are available at Bioinformatics online.

Economic African Development in the Context of FinTech

2020 ◽  
pp. 273-289
Author(s):  
Youssra Ben Romdhane ◽  
Sahar Loukil ◽  
Souhaila Kammoun
Keyword(s):  
Economic Growth ◽  
World Bank ◽  
Least Square ◽  
National Accounts ◽  
African Countries ◽  
The World Bank ◽  
Digital Infrastructure ◽  
Wide Range ◽  
The World ◽  
Data Files

The purpose of this chapter is to analyze the effect of FinTech and political incertitude on economic growth through a multiple regression. Thus, the authors employ the method of generalized least square (GLS) with panel data. The sample concerns 21 African countries during (2001-2014-2017). The authors use a wide range of measures from Global Findex Database 2017, the World Bank platform, the World Bank national accounts data, and the OECD National Accounts data files base in the context of Africa. Empirical results show that FinTech is a driver of economic growth unless it is actively used in a developed digital infrastructure. In fact, the authors prove that, when financial technologies are used in both transactions (receive and made digital payment), they significantly contribute to the economic cycle. Passive use like simple consumption actions are not a significant lever for the economy. The principal contribution is to highlight that the active use of financial innovations and not passive one and the developed digital infrastructure do promote economic growth in African countries.

scholarly journals simuG: a general-purpose genome simulator

2019 ◽  
Vol 35 (21) ◽  
pp. 4442-4444 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
Copy Number Variants ◽  
General Purpose ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Bioinformatics Analyses ◽  
Full Spectrum ◽  
Single Nucleotide ◽  
Genomic Variants ◽  
Wide Range ◽  
Simulation Based

Abstract Summary Simulated genomes with pre-defined and random genomic variants can be very useful for benchmarking genomic and bioinformatics analyses. Here we introduce simuG, a lightweight tool for simulating the full-spectrum of genomic variants (single nucleotide polymorphisms, Insertions/Deletions, copy number variants, inversions and translocations) for any organisms (including human). The simplicity and versatility of simuG make it a unique general-purpose genome simulator for a wide-range of simulation-based applications. Availability and implementation Code in Perl along with user manual and testing data is available at https://github.com/yjx1217/simuG. This software is free for use under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution

2020 ◽  
Vol 36 (9) ◽  
pp. 2929-2931 ◽  
Author(s):  
Hakan Ozadam ◽  
Michael Geng ◽  
Can Cenik
Keyword(s):  
Ribosome Profiling ◽  
Read Length ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Software Ecosystem ◽  
Binary File ◽  
Wide Range ◽  
Quality Control Metrics ◽  
Multiple Metrics ◽  
And Storage

Abstract Summary Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named ‘ribo’. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. Availability and implementation For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PSORTm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data

2020 ◽  
Vol 36 (10) ◽  
pp. 3043-3048 ◽  
Author(s):  
Michael A Peabody ◽  
Wing Yin Venus Lau ◽  
Gemma R Hoad ◽  
Baofeng Jia ◽  
Finlay Maguire ◽  
...  
Keyword(s):  
Water Quality ◽  
Subcellular Localization ◽  
Microbial Communities ◽  
Cell Envelope ◽  
Supplementary Information ◽  
Protein Subcellular Localization ◽  
Metagenomic Sequence ◽  
Archaeal Protein ◽  
Wide Range ◽  
Metagenomics Data

Abstract Motivation Many methods for microbial protein subcellular localization (SCL) prediction exist; however, none is readily available for analysis of metagenomic sequence data, despite growing interest from researchers studying microbial communities in humans, agri-food relevant organisms and in other environments (e.g. for identification of cell-surface biomarkers for rapid protein-based diagnostic tests). We wished to also identify new markers of water quality from freshwater samples collected from pristine versus pollution-impacted watersheds. Results We report PSORTm, the first bioinformatics tool designed for prediction of diverse bacterial and archaeal protein SCL from metagenomics data. PSORTm incorporates components of PSORTb, one of the most precise and widely used protein SCL predictors, with an automated classification by cell envelope. An evaluation using 5-fold cross-validation with in silico-fragmented sequences with known localization showed that PSORTm maintains PSORTb’s high precision, while sensitivity increases proportionately with metagenomic sequence fragment length. PSORTm’s read-based analysis was similar to PSORTb-based analysis of metagenome-assembled genomes (MAGs); however, the latter requires non-trivial manual classification of each MAG by cell envelope, and cannot make use of unassembled sequences. Analysis of the watershed samples revealed the importance of normalization and identified potential biomarkers of water quality. This method should be useful for examining a wide range of microbial communities, including human microbiomes, and other microbiomes of medical, environmental or industrial importance. Availability and implementation Documentation, source code and docker containers are available for running PSORTm locally at https://www.psort.org/psortm/ (freely available, open-source software under GNU General Public License Version 3). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MOVICS: an R package for multi-omics integration and visualization in cancer subtyping

2020 ◽  
Author(s):  
Xiaofan Lu ◽  
Jialin Meng ◽  
Yujie Zhou ◽  
Liyun Jiang ◽  
Fangrong Yan
Keyword(s):  
Clustering Algorithms ◽  
R Package ◽  
Supplementary Information ◽  
Multiple Perspectives ◽  
Model Free ◽  
Omics Integration ◽  
Wide Range ◽  
Breast Cancer Cohort ◽  
The One ◽  
Minimal Effort

Abstract Summary Stratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here, we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using two model-free approaches for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. By analysing two published breast cancer cohort, we signifies that MOVICS can serve a wide range of users and assist cancer therapy by moving away from the ‘one-size-fits-all’ approach to patient care. Availability and implementation MOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS. Supplementary information Supplementary data are available at Bioinformatics online.

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