ALeS: Adaptive-length spaced-seed design

2020 ◽  
Author(s):  
Arnab Mallik ◽  
Lucian Ilie
Keyword(s):  
Heuristic Algorithms ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Supplementary Information ◽  
Biological Research ◽  
Art Programs ◽  
Current State ◽  
Spaced Seeds ◽  
Highly Sensitive ◽  
Blast Program

Abstract Motivation Sequence similarity is the most frequently used procedure in biological research, as proved by the widely used BLAST program. The consecutive seed used by BLAST can be dramatically improved by considering multiple spaced seeds. Finding the best seeds is a hard problem and much effort went into developing heuristic algorithms and software for designing highly sensitive spaced seeds. Results We introduce a new algorithm and software, ALeS, that produces more sensitive seeds than the current state-of-the-art programs, as shown by extensive testing. We also accurately estimate the sensitivity of a seed, enabling its computation for arbitrary seeds. Availability The source code is freely available at github.com/lucian-ilie/ALeS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Joint epitope selection and spacer design for string-of-beads vaccines

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i643-i650
Author(s):  
Emilio Dorigatti ◽  
Benjamin Schubert
Keyword(s):  
Antigen Processing ◽  
State Of The Art ◽  
Supplementary Information ◽  
Current State ◽  
Fixed Set ◽  
Epitope Selection ◽  
High Chance ◽  
Selection Of ◽  
Design Steps

Abstract Motivation Conceptually, epitope-based vaccine design poses two distinct problems: (i) selecting the best epitopes to elicit the strongest possible immune response and (ii) arranging and linking them through short spacer sequences to string-of-beads vaccines, so that their recovery likelihood during antigen processing is maximized. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two design steps, usually emphasizing theoretical immunogenicity over correct vaccine processing, thus resulting in vaccines with less effective immunogenicity in vivo. Results In this work, we present a computational approach based on linear programming, called JessEV, that solves both design steps simultaneously, allowing to weigh the selection of a set of epitopes that have great immunogenic potential against their assembly into a string-of-beads construct that provides a high chance of recovery. We conducted Monte Carlo cleavage simulations to show that a fixed set of epitopes often cannot be assembled adequately, whereas selecting epitopes to accommodate proper cleavage requirements substantially improves their recovery probability and thus the effective immunogenicity, pathogen and population coverage of the resulting vaccines by at least 2-fold. Availability and implementation The software and the data analyzed are available at https://github.com/SchubertLab/JessEV. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Rapid discovery of novel prophages using biological feature engineering and machine learning

2020 ◽  
Author(s):  
Kimmo Sirén ◽  
Andrew Millard ◽  
Bent Petersen ◽  
M Thomas P Gilbert ◽  
Martha RJ Clokie ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Feature Space ◽  
Machine Learning Method ◽  
Biological Feature ◽  
Bacterial Genomes ◽  
Current State ◽  
Starting Point ◽  
Feature Based

ABSTRACTProphages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

EWASex: an efficient R-package to predict sex in epigenome-wide association studies

2020 ◽  
Author(s):  
Jesper Beltoft Lund ◽  
Weilong Li ◽  
Afsaneh Mohammadnejad ◽  
Shuxia Li ◽  
Jan Baumbach ◽  
...  
Keyword(s):  
X Chromosome ◽  
State Of The Art ◽  
Association Studies ◽  
R Package ◽  
Important Variable ◽  
Supplementary Information ◽  
Sex Estimation ◽  
Current State ◽  
Powerful Approach ◽  
Small Set

Abstract Summary Epigenome-Wide Association Study (EWAS) has become a powerful approach to identify epigenetic variations associated with diseases or health traits. Sex is an important variable to include in EWAS to ensure unbiased data processing and statistical analysis. We introduce the R-package EWASex, which allows for fast and highly accurate sex-estimation using DNA methylation data on a small set of CpG sites located on the X-chromosome under stable X-chromosome inactivation in females. Results We demonstrate that EWASex outperforms the current state of the art tools by using different EWAS datasets. With EWASex, we offer an efficient way to predict and to verify sex that can be easily implemented in any EWAS using blood samples or even other tissue types. It comes with pre-trained weights to work without prior sex labels and without requiring access to RAW data, which is a necessity for all currently available methods. Availability and implementation The EWASex R-package along with tutorials, documentation and source code are available at https://github.com/Silver-Hawk/EWASex. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals An enhanced isothermal amplification assay for viral detection

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jason Qian ◽  
Sarah A. Boswell ◽  
Christopher Chidley ◽  
Zhi-xiang Lu ◽  
Mary E. Pettit ◽  
...  
Keyword(s):  
Detection Limit ◽  
Genomic Sequence ◽  
State Of The Art ◽  
Molecular Diagnostic ◽  
Sample Collection ◽  
Rna Purification ◽  
Current State ◽  
Highly Sensitive ◽  
Amplification Assay ◽  
Molecular Diagnostic Test

AbstractRapid, inexpensive, robust diagnostics are essential to control the spread of infectious diseases. Current state of the art diagnostics are highly sensitive and specific, but slow, and require expensive equipment. Here we report the development of a molecular diagnostic test for SARS-CoV-2 based on an enhanced recombinase polymerase amplification (eRPA) reaction. eRPA has a detection limit on patient samples down to 5 viral copies, requires minimal instrumentation, and is highly scalable and inexpensive. eRPA does not cross-react with other common coronaviruses, does not require RNA purification, and takes ~45 min from sample collection to results. eRPA represents a first step toward at-home SARS-CoV-2 detection and can be adapted to future viruses within days of genomic sequence availability.

scholarly journals Helixer: Cross-species gene annotation of large eukaryotic genomes using deep learning

2020 ◽  
Author(s):  
Felix Stiehler ◽  
Marvin Steinborn ◽  
Stephan Scholz ◽  
Daniela Dey ◽  
Andreas P M Weber ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
State Of The Art ◽  
Gene Annotation ◽  
Land Plant ◽  
Supplementary Information ◽  
Current State ◽  
Learning Capabilities ◽  
Vertebrate Model ◽  
Eukaryotic Genomes

Abstract Motivation Current state-of-the-art tools for the de novo annotation of genes in eukaryotic genomes have to be specifically fitted for each species and still often produce annotations that can be improved much further. The fundamental algorithmic architecture for these tools has remained largely unchanged for about two decades, limiting learning capabilities. Here, we set out to improve the cross-species annotation of genes from DNA sequence alone with the help of deep learning. The goal is to eliminate the dependency on a closely related gene model while also improving the predictive quality in general with a fundamentally new architecture. Results We present Helixer, a framework for the development and usage of a cross-species deep learning model that improves significantly on performance and generalizability when compared to more traditional methods. We evaluate our approach by building a single vertebrate model for the base-wise annotation of 186 animal genomes and a separate land plant model for 51 plant genomes. Our predictions are shown to be much less sensitive to the length of the genome than those of a current state-of-the-art tool. We also present two novel post-processing techniques that each worked to further strengthen our annotations and show in-depth results of an RNA-Seq based comparison of our predictions. Our method does not yet produce comprehensive gene models but rather outputs base pair wise probabilities. Availability The source code of this work is available at https://github.com/weberlab-hhu/Helixer under the GNU General Public License v3.0. The trained models are available at https://doi.org/10.5281/zenodo.3974409 Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Innovative method for reducing uninformative calls in non-invasive prenatal testing

2018 ◽  
Author(s):  
Jaroslav Budis ◽  
Juraj Gazdarica ◽  
Jan Radvanszky ◽  
Gabor Szucs ◽  
Marcel Kucharik ◽  
...  
Keyword(s):  
State Of The Art ◽  
False Negative ◽  
Prenatal Testing ◽  
Obstetric Care ◽  
High Sensitivity ◽  
Supplementary Information ◽  
Non Invasive ◽  
Current State ◽  
Z Scores ◽  
Supplementary Material

AbstractMotivationNon-invasive prenatal testing or NIPT is currently among the top researched topic in obstetric care. While the performance of the current state-of-the-art NIPT solutions achieve high sensitivity and specificity, they still struggle with a considerable number of samples that cannot be concluded with certainty. Such uninformative results are often subject to repeated blood sampling and re-analysis, usually after two weeks, and this period may cause a stress to the future mothers as well as increase the overall cost of the test.ResultsWe propose a supplementary method to traditional z-scores to reduce the number of such uninformative calls. The method is based on a novel analysis of the length profile of circulating cell free DNA which compares the change in such profiles when random-based and length-based elimination of some fragments is performed. The proposed method is not as accurate as the standard z-score; however, our results suggest that combination of these two independent methods correctly resolves a substantial portion of healthy samples with an uninformative result. Additionally, we discuss how the proposed method can be used to identify maternal aberrations, thus reducing the risk of false positive and false negative calls.Availability and ImplementationA particular implementation of the proposed methods is not provided with the manuscript.ContactCorrespondence regarding the manuscript should be directed at Frantisek Duris ([email protected]).Supplementary InformationNo additional supplementary information is available.

Challenges in gene-oriented approaches for pangenome content discovery

2020 ◽  
Author(s):  
Vincenzo Bonnici ◽  
Emiliano Maresi ◽  
Rosalba Giugno
Keyword(s):  
State Of The Art ◽  
Supplementary Information ◽  
Computational Pipeline ◽  
Bacterial Populations ◽  
Genomic Features ◽  
The Past ◽  
Current State ◽  
Content Discovery ◽  
Review Reports ◽  
The Given

Abstract Given a group of genomes, represented as the sets of genes that belong to them, the discovery of the pangenomic content is based on the search of genetic homology among the genes for clustering them into families. Thus, pangenomic analyses investigate the membership of the families to the given genomes. This approach is referred to as the gene-oriented approach in contrast to other definitions of the problem that takes into account different genomic features. In the past years, several tools have been developed to discover and analyse pangenomic contents. Because of the hardness of the problem, each tool applies a different strategy for discovering the pangenomic content. This results in a differentiation of the performance of each tool that depends on the composition of the input genomes. This review reports the main analysis instruments provided by the current state of the art tools for the discovery of pangenomic contents. Moreover, unlike previous works, the presented study compares pangenomic tools from a methodological perspective, analysing the causes that lead a given methodology to outperform other tools. The analysis is performed by taking into account different bacterial populations, which are synthetically generated by changing evolutionary parameters. The benchmarks used to compare the pangenomic tools, in addition to the computational pipeline developed for this purpose, are available at https://github.com/InfOmics/pangenes-review. Contact: V. Bonnici, R. Giugno Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

scholarly journals Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data

2020 ◽  
Author(s):  
Andrew E Teschendorff ◽  
Alok K Maity ◽  
Xue Hu ◽  
Chen Weiyan ◽  
Matthias Lechner
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Computational Cost ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Current State ◽  
Multipotent Cells ◽  
Comparable Accuracy

Abstract Motivation An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. Availability and implementation CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. Supplementary information Supplementary data are available at Bioinformatics online.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

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