EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences

Supplementary Data ◽

Comparable Accuracy ◽

Meta Analyses ◽

Adapter Trimming ◽

Abstract Motivation Cross-sample comparisons or large-scale meta-analyses based on the next generation sequencing (NGS) involve replicable and universal data preprocessing, including removing adapter fragments in contaminated reads (i.e. adapter trimming). While modern adapter trimmers require users to provide candidate adapter sequences for each sample, which are sometimes unavailable or falsely documented in the repositories (such as GEO or SRA), large-scale meta-analyses are therefore jeopardized by suboptimal adapter trimming. Results Here we introduce a set of fast and accurate adapter detection and trimming algorithms that entail no a priori adapter sequences. These algorithms were implemented in modern C++ with SIMD and multithreading to accelerate its speed. Our experiments and benchmarks show that the implementation (i.e. EARRINGS), without being given any hint of adapter sequences, can reach comparable accuracy and higher throughput than that of existing adapter trimmers. EARRINGS is particularly useful in meta-analyses of a large batch of datasets and can be incorporated in any sequence analysis pipelines in all scales. Availability and implementation EARRINGS is open-source software and is available at https://github.com/jhhung/EARRINGS. Supplementary information Supplementary data are available at Bioinformatics online.

ngsReports: a Bioconductor package for managing FastQC reports and other NGS related log files

Bioinformatics ◽

10.1093/bioinformatics/btz937 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2587-2588 ◽

Cited By ~ 10

Author(s):

Christopher M Ward ◽

Thu-Hien To ◽

Stephen M Pederson

Keyword(s):

Quality Control ◽

R Package ◽

Bioconductor Package ◽

Supplementary Data ◽

Large Sample ◽

Log Files ◽

Shiny App ◽

Abstract Motivation High throughput next generation sequencing (NGS) has become exceedingly cheap, facilitating studies to be undertaken containing large sample numbers. Quality control (QC) is an essential stage during analytic pipelines and the outputs of popular bioinformatics tools such as FastQC and Picard can provide information on individual samples. Although these tools provide considerable power when carrying out QC, large sample numbers can make inspection of all samples and identification of systemic bias a challenge. Results We present ngsReports, an R package designed for the management and visualization of NGS reports from within an R environment. The available methods allow direct import into R of FastQC reports along with outputs from other tools. Visualization can be carried out across many samples using default, highly customizable plots with options to perform hierarchical clustering to quickly identify outlier libraries. Moreover, these can be displayed in an interactive shiny app or HTML report for ease of analysis. Availability and implementation The ngsReports package is available on Bioconductor and the GUI shiny app is available at https://github.com/UofABioinformaticsHub/shinyNgsreports. Supplementary information Supplementary data are available at Bioinformatics online.

MsPAC: a tool for haplotype-phased structural variant detection

Bioinformatics ◽

10.1093/bioinformatics/btz618 ◽

2019 ◽

Vol 36 (3) ◽

pp. 922-924 ◽

Cited By ~ 3

Author(s):

Oscar L Rodriguez ◽

Anna Ritz ◽

Andrew J Sharp ◽

Ali Bashir

Keyword(s):

Genomic Data ◽

Supplementary Data ◽

High Quality ◽

Structural Variant ◽

Long Read ◽

One Step ◽

Variant Detection ◽

Abstract Summary While next-generation sequencing (NGS) has dramatically increased the availability of genomic data, phased genome assembly and structural variant (SV) analyses are limited by NGS read lengths. Long-read sequencing from Pacific Biosciences and NGS barcoding from 10x Genomics hold the potential for far more comprehensive views of individual genomes. Here, we present MsPAC, a tool that combines both technologies to partition reads, assemble haplotypes (via existing software) and convert assemblies into high-quality, phased SV predictions. MsPAC represents a framework for haplotype-resolved SV calls that moves one step closer to fully resolved, diploid genomes. Availability and implementation https://github.com/oscarlr/MsPAC. Supplementary information Supplementary data are available at Bioinformatics online.

SnakeChunks: modular blocks to build Snakemake workflows for reproducible NGS analyses

10.1101/165191 ◽

2017 ◽

Author(s):

Claire Rioualen ◽

Lucie Charbonnier-Khamvongsa ◽

Jacques van Helden

Keyword(s):

Next Generation Sequencing ◽

Life Sciences ◽

Supplementary Data ◽

Rna Seq ◽

Genome Wide ◽

Domains Of Life ◽

Supplementary Material ◽

AbstractSummaryNext-Generation Sequencing (NGS) is becoming a routine approach for most domains of life sciences, yet there is a crucial need to improve the automation of processing for the huge amounts of data generated and to ensure reproducible results. We present SnakeChunks, a collection of Snakemake rules enabling to compose modular and user-configurable workflows, and show its usage with analyses of transcriptome (RNA-seq) and genome-wide location (ChIP-seq) data.AvailabilityThe code is freely available (github.com/SnakeChunks/SnakeChunks), and documented with tutorials and illustrative demos (snakechunks.readthedocs.io)[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa559 ◽

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 ◽

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.

OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

Bioinformatics ◽

10.1093/bioinformatics/btaa274 ◽

2020 ◽

Vol 36 (13) ◽

pp. 4097-4098 ◽

Cited By ~ 3

Author(s):

Anna Breit ◽

Simon Ott ◽

Asan Agibetov ◽

Matthias Samwald

Keyword(s):

Link Prediction ◽

Large Scale ◽

Source Code ◽

Machine Learning Algorithms ◽

Knowledge Networks ◽

Supplementary Data ◽

Biomedical Knowledge ◽

High Quality ◽

Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

PyMethylProcess—convenient high-throughput preprocessing workflow for DNA methylation data

Bioinformatics ◽

10.1093/bioinformatics/btz594 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5379-5381 ◽

Cited By ~ 2

Author(s):

Joshua J Levy ◽

Alexander J Titus ◽

Lucas A Salas ◽

Brock C Christensen

Keyword(s):

Large Scale ◽

Scale Production ◽

Methylation Data ◽

Supplementary Data ◽

Data Preparation ◽

Methylation Array ◽

Project Home Page ◽

Large Scale Production ◽

Set Up

Abstract Summary Performing highly parallelized preprocessing of methylation array data using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. We present a highly reproducible, scalable pipeline (PyMethylProcess) that can be quickly set-up and deployed through Docker and PIP. Availability and implementation Project Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI (pymethylprocess), Docker (joshualevy44/pymethylprocess). Supplementary information Supplementary data are available at Bioinformatics online.

Quantification of aneuploidy in targeted sequencing data using ASCETS

Bioinformatics ◽

10.1093/bioinformatics/btaa980 ◽

2020 ◽

Author(s):

Liam F Spurr ◽

Mehdi Touat ◽

Alison M Taylor ◽

Adrian M Dubuc ◽

Juliann Shih ◽

...

Keyword(s):

Copy Number ◽

Large Scale ◽

Genomic Analysis ◽

Targeted Sequencing ◽

Supplementary Data ◽

Sequencing Data ◽

Copy Number Changes ◽

Panel Sequencing ◽

Chromosome Level

Abstract Summary The expansion of targeted panel sequencing efforts has created opportunities for large-scale genomic analysis, but tools for copy-number quantification on panel data are lacking. We introduce ASCETS, a method for the efficient quantitation of arm and chromosome-level copy-number changes from targeted sequencing data. Availability and implementation ASCETS is implemented in R and is freely available to non-commercial users on GitHub: https://github.com/beroukhim-lab/ascets, along with detailed documentation. Supplementary information Supplementary data are available at Bioinformatics online.

RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

Bioinformatics ◽

10.1093/bioinformatics/btab683 ◽

2021 ◽

Author(s):

Zachary B Abrams ◽

Dwayne G Tally ◽

Lynne V Abruzzo ◽

Kevin R Coombes

Keyword(s):

Large Scale ◽

International System ◽

Genetic Data ◽

R Package ◽

Supplementary Data ◽

Computational Tools ◽

Text Format ◽

Karyotype Analyses ◽

Computational Analyses

Abstract Summary Cytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. Availability and Implementation Freely available at https://CRAN.R-project.org/package=RCytoGPS. The code for the underlying CytoGPS software can be found at https://github.com/i2-wustl/CytoGPS. Supplementary information There is no supplementary data.

NGS and phenotypic ontology-based approaches increase the diagnostic yield in syndromic retinal diseases

Human Genetics ◽

10.1007/s00439-021-02343-7 ◽

2021 ◽

Author(s):

I. Perea-Romero ◽

F. Blanco-Kelly ◽

I. Sanchez-Navarro ◽

I. Lorda-Sanchez ◽

S. Tahsin-Swafiri ◽

...

Keyword(s):

Exome Sequencing ◽

Diagnostic Yield ◽

A Priori ◽

Retinal Diseases ◽

Human Phenotype ◽

Whole Exome ◽

Generation Sequencing ◽

Clinical Category ◽

Selection Of

AbstractSyndromic retinal diseases (SRDs) are a group of complex inherited systemic disorders, with challenging molecular underpinnings and clinical management. Our main goal is to improve clinical and molecular SRDs diagnosis, by applying a structured phenotypic ontology and next-generation sequencing (NGS)-based pipelines. A prospective and retrospective cohort study was performed on 100 probands with an a priori diagnosis of non-Usher SRDs, using available clinical data, including Human Phenotype Ontology annotation, and further classification into seven clinical categories (ciliopathies, specific syndromes and five others). Retrospective molecular diagnosis was assessed using different molecular and bioinformatic methods depending on availability. Subsequently, uncharacterized probands were prospectively screened using other NGS approaches to extend the number of analyzed genes. After phenotypic classification, ciliopathies were the most common SRD (35%). A global characterization rate of 52% was obtained, with six cases incompletely characterized for a gene that partially explained the phenotype. An improved characterization rate was achieved addressing prospective cases (83%) and well-recognizable syndrome (62%) subgroups. The 27% of the fully characterized cases were reclassified into a different clinical category after identification of the disease-causing gene. Clinical-exome sequencing is the most appropriate first-tier approach for prospective cases, whereas whole-exome sequencing and bioinformatic reanalysis increases the diagnosis of uncharacterized retrospective cases to 45%, mostly those with unspecific symptoms. Our study describes a comprehensive approach to SRDs in daily clinical practice and the importance of thorough clinical assessment and selection of the most appropriate molecular test to be used to solve these complex cases and elucidate novel associations.

Next-generation sequencing in neuropathological diagnosis of infections of the nervous system

10.1101/039222 ◽

2016 ◽

Author(s):

Steven L. Salzberg ◽

Florian Breitwieser ◽

Anupama Kumar ◽

Haiping Hao ◽

Peter Burger ◽

...

Keyword(s):

Spinal Cord ◽

Nervous System ◽

Next Generation Sequencing ◽

Large Scale ◽

Infectious Agent ◽

Next Generation ◽

Dna And Rna ◽

Wide Range ◽

Objective: To determine the feasibility of next-generation sequencing (NGS) microbiome approaches in the diagnosis of infectious disorders in brain or spinal cord biopsies in patients with suspected central nervous system (CNS) infections. Methods: In a prospective-pilot study, we applied NGS in combination with a new computational analysis pipeline to detect the presence of pathogenic microbes in brain or spinal cord biopsies from ten patients with neurological problems indicating possible infection but for whom conventional clinical and microbiology studies yielded negative or inconclusive results. Results: Direct DNA and RNA sequencing of brain tissue biopsies generated 8.3 million to 29.1 million sequence reads per sample, which successfully identified with high confidence the infectious agent in three patients, identified possible pathogens in two more, and helped to understand neuropathological processes in three others, demonstrating the power of large-scale unbiased sequencing as a novel diagnostic tool. Validation techniques confirmed the pathogens identified by NGS in each of the three positive cases. Clinical outcomes were consistent with the findings yielded by NGS on the presence or absence of an infectious pathogenic process in eight of ten cases, and were non-contributory in the remaining two. Conclusions: NGS-guided metagenomic studies of brain, spinal cord or meningeal biopsies offer the possibility for dramatic improvements in our ability to detect (or rule out) a wide range of CNS pathogens, with potential benefits in speed, sensitivity, and cost. NGS-based microbiome approaches present a major new opportunity to investigate the potential role of infectious pathogens in the pathogenesis of neuroinflammatory disorders.