scholarly journals 6mA-Finder: a novel online tool for predicting DNA N6-methyladenine sites in genomes

2020 ◽  
Vol 36 (10) ◽  
pp. 3257-3259 ◽  
Author(s):  
Haodong Xu ◽  
Ruifeng Hu ◽  
Peilin Jia ◽  
Zhongming Zhao
Keyword(s):  
Molecular Mechanisms ◽  
Epigenetic Modification ◽  
Supplementary Information ◽  
Recursive Feature Elimination ◽  
Supplementary Data ◽  
Online Tool ◽  
Crucial Step ◽  
Cellular Processes ◽  
Multiple Cross ◽  
Species Specific

Abstract Motivation DNA N6-methyladenine (6 mA) has recently been found as an essential epigenetic modification, playing its roles in a variety of cellular processes. The abnormal status of DNA 6 mA modification has been reported in cancer and other disease. The annotation of 6 mA marks in genome is the first crucial step to explore the underlying molecular mechanisms including its regulatory roles. Results We present a novel online DNA 6 mA site tool, 6 mA-Finder, by incorporating seven sequence-derived information and three physicochemical-based features through recursive feature elimination strategy. Our multiple cross-validations indicate the promising accuracy and robustness of our model. 6 mA-Finder outperforms its peer tools in general and species-specific 6 mA site prediction, suggesting it can provide a useful resource for further experimental investigation of DNA 6 mA modification. Availability and implementation https://bioinfo.uth.edu/6mA_Finder. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TandemTools: mapping long reads and assessing/improving assembly quality in extra-long tandem repeats

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i75-i83 ◽  
Author(s):  
Alla Mikheenko ◽  
Andrey V Bzikadze ◽  
Alexey Gurevich ◽  
Karen H Miga ◽  
Pavel A Pevzner
Keyword(s):  
Quality Assessment ◽  
Chromosome Segregation ◽  
Tandem Repeats ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Assembly Quality ◽  
Cellular Processes ◽  
Long Reads ◽  
Long Read ◽  
Eukaryotic Genomes

Abstract Motivation Extra-long tandem repeats (ETRs) are widespread in eukaryotic genomes and play an important role in fundamental cellular processes, such as chromosome segregation. Although emerging long-read technologies have enabled ETR assemblies, the accuracy of such assemblies is difficult to evaluate since there are no tools for their quality assessment. Moreover, since the mapping of error-prone reads to ETRs remains an open problem, it is not clear how to polish draft ETR assemblies. Results To address these problems, we developed the TandemTools software that includes the TandemMapper tool for mapping reads to ETRs and the TandemQUAST tool for polishing ETR assemblies and their quality assessment. We demonstrate that TandemTools not only reveals errors in ETR assemblies but also improves the recently generated assemblies of human centromeres. Availability and implementation https://github.com/ablab/TandemTools. Supplementary information Supplementary data are available at Bioinformatics online.

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

2021 ◽  
Author(s):  
Yuanyuan Han ◽  
Lan Huang ◽  
Fengfeng Zhou
Keyword(s):  
Experimental Data ◽  
Feature Selection ◽  
The Other ◽  
Supplementary Information ◽  
Recursive Feature Elimination ◽  
Supplementary Data ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Class Labels ◽  
Selection Algorithms

Abstract Motivation A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets. Results The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc = 1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424 and 0.8601 for the other three methylome datasets GSE74845, GSE103186 and GSE80970, respectively. Four transcriptome datasets received Acc = 1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917. Availability and implementation The experiments in this study are implemented and tested using the programming language Python version 3.7.6. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Predicting promoters in phage genomes using PhagePromoter

2019 ◽  
Vol 35 (24) ◽  
pp. 5301-5302 ◽  
Author(s):  
Marta Sampaio ◽  
Miguel Rocha ◽  
Hugo Oliveira ◽  
Oscar Dias
Keyword(s):  
Machine Learning ◽  
Genome Annotation ◽  
Antibacterial Agents ◽  
Supplementary Information ◽  
Promoter Prediction ◽  
Supplementary Data ◽  
Online Tool ◽  
Machine Learning Methods ◽  
Online Tools ◽  
The Galaxy

Abstract Summary The growing interest in phages as antibacterial agents has led to an increase in the number of sequenced phage genomes, increasing the need for intuitive bioinformatics tools for performing genome annotation. The identification of phage promoters is indeed the most difficult step of this process. Due to the lack of online tools for phage promoter prediction, we developed PhagePromoter, a tool for locating promoters in phage genomes, using machine learning methods. This is the first online tool for predicting promoters that uses phage promoter data and the first to identify both host and phage promoters with different motifs. Availability and implementation This tool was integrated in the Galaxy framework and it is available online at: https://bit.ly/2Dfebfv. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals WDSPdb: an updated resource for WD40 proteins

2019 ◽  
Vol 35 (22) ◽  
pp. 4824-4826 ◽  
Author(s):  
Jing Ma ◽  
Ke An ◽  
Jing-Bo Zhou ◽  
Nuo-Si Wu ◽  
Yang Wang ◽  
...  
Keyword(s):  
Web Site ◽  
Large Family ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Wd40 Repeat ◽  
Tertiary Structures ◽  
Cellular Processes ◽  
1000 Genomes ◽  
Repeat Proteins ◽  
The Web

AbstractSummaryThe WD40-repeat proteins are a large family of scaffold molecules that assemble complexes in various cellular processes. Obtaining their structures is the key to understanding their interaction details. We present WDSPdb 2.0, a significantly updated resource providing accurately predicted secondary and tertiary structures and featured sites annotations. Based on an optimized pipeline, WDSPdb 2.0 contains about 600 thousand entries, an increase of 10-fold, and integrates more than 37 000 variants from sources of ClinVar, Cosmic, 1000 Genomes, ExAC, IntOGen, cBioPortal and IntAct. In addition, the web site is largely improved for visualization, exploring and data downloading.Availability and implementationhttp://www.wdspdb.com/wdsp/ or http://wu.scbb.pkusz.edu.cn/wdsp/.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals ccNetViz: a WebGL-based JavaScript library for visualization of large networks

2020 ◽  
Vol 36 (16) ◽  
pp. 4527-4529
Author(s):  
Ales Saska ◽  
David Tichy ◽  
Robert Moore ◽  
Achilles Rasquinha ◽  
Caner Akdas ◽  
...  
Keyword(s):  
Systems Biology ◽  
Complex Networks ◽  
Open Source ◽  
High Speed ◽  
A Priori ◽  
Supplementary Information ◽  
Network Visualization ◽  
Supplementary Data ◽  
Web Based ◽  
Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. 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.

KEC: unique sequence search by K-mer exclusion

2021 ◽  
Author(s):  
Pavel Beran ◽  
Dagmar Stehlíková ◽  
Stephen P Cohen ◽  
Vladislav Čurn
Keyword(s):  
Amino Acid ◽  
Nucleic Acid ◽  
Source Code ◽  
Unique Sequence ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Laptop Computers ◽  
Sequence Search ◽  
Target Sequences ◽  
Cross Reference

Abstract Summary Searching for amino acid or nucleic acid sequences unique to one organism may be challenging depending on size of the available datasets. K-mer elimination by cross-reference (KEC) allows users to quickly and easily find unique sequences by providing target and non-target sequences. Due to its speed, it can be used for datasets of genomic size and can be run on desktop or laptop computers with modest specifications. Availability and implementation KEC is freely available for non-commercial purposes. Source code and executable binary files compiled for Linux, Mac and Windows can be downloaded from https://github.com/berybox/KEC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Role of Long Non-Coding RNA Polymorphisms in Cancer Chemotherapeutic Response

2021 ◽  
Vol 11 (6) ◽  
pp. 513
Author(s):  
Zheng Zhang ◽  
Meng Gu ◽  
Zhongze Gu ◽  
Yan-Ru Lou
Keyword(s):  
Molecular Mechanisms ◽  
Neurological Diseases ◽  
Cellular Processes ◽  
Dna And Rna ◽  
Chemotherapeutic Response ◽  
Non Coding Rna ◽  
Response To Chemotherapy ◽  
Personalized Oncology ◽  
Long Non Coding Rna

Genetic polymorphisms are defined as the presence of two or more different alleles in the same locus, with a frequency higher than 1% in the population. Since the discovery of long non-coding RNAs (lncRNAs), which refer to a non-coding RNA with a length of more than 200 nucleotides, their biological roles have been increasingly revealed in recent years. They regulate many cellular processes, from pluripotency to cancer. Interestingly, abnormal expression or dysfunction of lncRNAs is closely related to the occurrence of human diseases, including cancer and degenerative neurological diseases. Particularly, their polymorphisms have been found to be associated with altered drug response and/or drug toxicity in cancer treatment. However, molecular mechanisms are not yet fully elucidated, which are expected to be discovered by detailed studies of RNA–protein, RNA–DNA, and RNA–lipid interactions. In conclusion, lncRNAs polymorphisms may become biomarkers for predicting the response to chemotherapy in cancer patients. Here we review and discuss how gene polymorphisms of lncRNAs affect cancer chemotherapeutic response. This knowledge may pave the way to personalized oncology treatments.

scholarly journals DEAD-box helicases modulate dicing body formation in Arabidopsis

2021 ◽  
Vol 7 (18) ◽  
pp. eabc6266
Author(s):  
Qi Li ◽  
Ningkun Liu ◽  
Qing Liu ◽  
Xingguo Zheng ◽  
Lu Lu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Turnip Mosaic Virus ◽  
Liquid Droplets ◽  
Phase Separations ◽  
Body Formation ◽  
Cellular Processes ◽  
Dead Box ◽  
Body Components ◽  
Liquid Phase Separations ◽  
Spatiotemporal Control

Eukaryotic cells contain numerous membraneless organelles that are made from liquid droplets of proteins and nucleic acids and that provide spatiotemporal control of various cellular processes. However, the molecular mechanisms underlying the formation and rapid stress-induced alterations of these organelles are relatively uncharacterized. Here, we investigated the roles of DEAD-box helicases in the formation and alteration of membraneless nuclear dicing bodies (D-bodies) in Arabidopsis thaliana. We uncovered that RNA helicase 6 (RH6), RH8, and RH12 are previously unidentified D-body components. These helicases interact with and promote the phase separation of SERRATE, a key component of D-bodies, and drive the formation of D-bodies through liquid-liquid phase separations (LLPSs). The accumulation of these helicases in the nuclei decreases upon Turnip mosaic virus infections, which couples with the decrease of D-bodies. Our results thus reveal the key roles of RH6, RH8, and RH12 in modulating D-body formation via LLPSs.

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