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Trending Papers in bioinformatics

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202
Published: Dec 2019
Published: Dec 2019
  • Seven years have passed since publication of our original paper on the development of a database course in bioinformatics curriculum. In this paper, we re-examine this course for its offering during 2005-2017.
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ayotune adebayo
204
Published: Dec 2020
Published: Dec 2020
  • COVID-19 caused by a novel coronavirus, a severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has recently broken out worldwide. Up to now, the development of vaccine is still in the stage of clinical research, and there is no clinically approved specific antiviral drug for human coronavirus infection. The purpose of this study is to investigate the key molecules involved in response during SARS-CoV-2 infection and provide references for the treatment of SARS-CoV-2.
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ayotune adebayo
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ayotune adebayo
203
Published: Dec 2020
Published: Dec 2020
  • Recently, the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), has caused an outbreak of respiratory illness, named corona virus disease 2019 (COVID-9), that was first detected in Wuhan City, Hubei Province, China.
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ayotune adebayo
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ayotune adebayo
203
Published: Mar 2021
Published: Mar 2021
  • Next Generation Sequencing (NGS) technology has revolutionized the study of human genetic code, enabling a fast, reliable, and cost-effect method for reading the genome.
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ayotune adebayo
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ayotune adebayo
370
Authors: Srivastava, Avi, et al
Published: Jan 2021
Authors: Srivastava, Avi, et al
Published: Jan 2021
Transcript and gene quantification is the first stepin many RNA-seq analyses. While many factors and propertiesof experimental RNA-seq data likely contribute to differences inaccuracy between various approaches to quantification, it has been demonstrated that quantification accuracy generally benefits from considering, during alignment, potential genomic origins for se-quenced fragments that reside outside of the annotated transcriptome. Recently, Varabyou et al. demonstrated that the presenceof transcriptional noise leads to systematic errors in the ability oftools, particularly annotation-based ones, to accurately estimate transcript expression. Here, we confirm the findings of Varabyouet al. using the simulation framework they have provided. Using the same data, we also examine the methodology of Srivastava et al. as implemented in recent versions of salmon, and show thatit substantially enhances the accuracy of annotation-based transcript quantification in these data.
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Chukwuma Chidera
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Chukwuma Chidera
201
Authors: Hu, Yu, et al
Published: Jan 2021
Authors: Hu, Yu, et al
Published: Jan 2021
Long-read RNA sequencing (RNA-seq) technologies have made it possible to sequence full-length transcripts, facilitating the exploration of isoform-specific gene expression (isoform relative abundance and isoform-level TPM) over conventional short-read RNA-seq. However, long-read RNA-seq suffers from high per-base error rate, presence of chimeric reads or alternative alignments, and other biases, which require different analysis methods than short-read RNA-seq. Here we present LIQA (Long-read Isoform Quantification and Analysis), an Expectation-Maximization based statistical method to quantify isoform expression and detect differential alternative splicing (DAS) events using long-read RNA-seq data. Rather than summarizing isoform-specific read counts directly as done in short-read methods, LIQA incorporates base-pair quality score and isoform-specific read length information to assign different weights across reads, which reflects alignment confidence. Moreover, LIQA can detect DAS events between conditions using isoform usage estimates. We evaluated LIQAs performance on simulated data and demonstrated that it outperforms other approaches in characterizing isoforms with low read coverage and in detecting DAS events between two groups. We also generated one direct mRNA sequencing dataset and one cDNA sequencing dataset using the Oxford Nanopore long-read platform, both with paired short-read RNA-seq data and qPCR data on selected genes, and we demonstrated that LIQA performs well in isoform discovery and quantification. Finally, we evaluated LIQA on a PacBio dataset on esophageal squamous epithelial cells, and demonstrated that LIQA recovered DAS events that failed to be detected in short-read data. In summary, LIQA leverages the power of long-read RNA-seq and achieves higher accuracy in estimating isoform abundance than existing approaches, especially for isoforms with low coverage and biased read distribution. LIQA is freely available at https://github.com/WGLab/LIQA.
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Chukwuma Chidera
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Chukwuma Chidera
201
Authors: Yunfeng WANG, Haoliang XUE, Christine POURCEL, Yang DU, Daniel GAUTHERET
Published: Jan 2021
Authors: Yunfeng WANG, Haoliang XUE, Christine POURCEL, Yang DU, Daniel GAUTHERET
Published: Jan 2021
The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. Herein, we introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves a higher precision than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease.
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Chukwuma Chidera
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Chukwuma Chidera
201
Authors: Toshiyuki Oda
Published: Jan 2021
Authors: Toshiyuki Oda
Published: Jan 2021
SurfStamp is an application that is used to generate textures for surface models of proteins. The textures contain information about surface residues and the information is drawn directly on the 3D object of the models. This approach is more intuitive than the labeling functions that most three-dimensional (3D) structure viewers use to show residue information. Therefore, the use of this application enables researchers, readers, or audiences to easily determine which residues are contributing the surface they are focusing on.
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Chukwuma Chidera
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Chukwuma Chidera
201
Authors: Xavier Didelot, Erik Volz
Published: Jan 2021
Authors: Xavier Didelot, Erik Volz
Published: Jan 2021
Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. Non-parametric models for population dynamics combined with molecular clock models which relate genetic data to time have enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The theory for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the models to genetic data from several pathogen data sets.
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Chukwuma Chidera
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Chukwuma Chidera
202
Authors: Håkan Wieslander, Ankit Gupta, Ebba Bergman, Erik Hallström, Philip John Harrison
Published: Jan 2021
Authors: Håkan Wieslander, Ankit Gupta, Ebba Bergman, Erik Hallström, Philip John Harrison
Published: Jan 2021
Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images, various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images would get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images to enable virtual staining for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key indicators of successful nanoparticle uptake.
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Chukwuma Chidera
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Chukwuma Chidera
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