scholarly journals CRISPRCloud2: A cloud-based platform for deconvolving CRISPR screen data

2018 ◽  
Author(s):  
Hyun-Hwan Jeong ◽  
Seon Young Kim ◽  
Maxime W.C. Rousseaux ◽  
Huda Y. Zoghbi ◽  
Zhandong Liu
Keyword(s):  
Cost Effectiveness ◽  
High Throughput ◽  
State Of The Art ◽  
Statistical Test ◽  
Query Interface ◽  
Sequencing Data ◽  
Crispr Screen ◽  
One Stop ◽  
User Friendly ◽  
Screening Approaches

AbstractThe simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches available to many. However, the large amount of sequencing data derived from these studies yields often unwieldy datasets requiring considerable bioinformatic resources to deconvolute data; a feature which is simply not accessible to many wet labs. To address these needs, we have developed a cloud-based webtool CRISPRCloud2 that provides a state-of-the-art accuracy in mapping short reads to CRISPR library, a powerful statistical test that aggregates information across multiple sgRNAs targeting the same gene, a user-friendly data visualization and query interface, as well as easy linking to other CRISPR tools and bioinformatics resources for target prioritization. CRISPRCloud2 is a one-stop shop for labs analyzing CRISPR screen data.

scholarly journals hypeR: an R package for geneset enrichment workflows

2019 ◽  
Author(s):  
Anthony Federico ◽  
Stefano Monti
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Use Cases ◽  
Sequencing Data ◽  
Wide Audience ◽  
Popular Method ◽  
High Throughput Sequencing Data ◽  
One Stop ◽  
Recent Version

Abstract Summary Geneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases. Availability and implementation The most recent version of the package is available at https://github.com/montilab/hypeR. Contact [email protected] or [email protected]

scholarly journals COPAR: A ChIP-Seq Optimal Peak Analyzer

2017 ◽  
Vol 2017 ◽  
pp. 1-4
Author(s):  
Binhua Tang ◽  
Xihan Wang ◽  
Victor X. Jin
Keyword(s):  
High Throughput ◽  
Genomic Feature ◽  
Data Sets ◽  
Sequencing Data ◽  
Genomic Features ◽  
Peak Alignment ◽  
Chip Sequencing ◽  
Quality Check ◽  
User Friendly ◽  
High Throughput Experiments

Sequencing data quality and peak alignment efficiency of ChIP-sequencing profiles are directly related to the reliability and reproducibility of NGS experiments. Till now, there is no tool specifically designed for optimal peak alignment estimation and quality-related genomic feature extraction for ChIP-sequencing profiles. We developed open-sourced COPAR, a user-friendly package, to statistically investigate, quantify, and visualize the optimal peak alignment and inherent genomic features using ChIP-seq data from NGS experiments. It provides a versatile perspective for biologists to perform quality-check for high-throughput experiments and optimize their experiment design. The package COPAR can process mapped ChIP-seq read file in BED format and output statistically sound results for multiple high-throughput experiments. Together with three public ChIP-seq data sets verified with the developed package, we have deposited COPAR on GitHub under a GNU GPL license.

scholarly journals Efficient exact associative structure for sequencing data

2019 ◽  
Author(s):  
Camille Marchet ◽  
Mael Kerbiriou ◽  
Antoine Limasset
Keyword(s):  
High Throughput ◽  
Scale Up ◽  
Index Structure ◽  
De Bruijn Graph ◽  
Compact Representation ◽  
Sequencing Data ◽  
Transcriptomic Sequencing ◽  
Multiple Cores ◽  
De Bruijn ◽  
User Friendly

AbstractMotivationA plethora of methods and applications share the fundamental need to associate information to words for high throughput sequence analysis. Indexing billions of k-mers is promptly a scalability problem, as exact associative indexes can be memory expensive. Recent works take advantage of the properties of the k-mer sets to leverage this challenge. They exploit the overlaps shared among k-mers by using a de Bruijn graph as a compact k-mer set to provide lightweight structures.ResultsWe present Blight, a static and exact index structure able to associate unique identifiers to indexed k-mers and to reject alien k-mers that scales to the largest kmer sets with a low memory cost. The proposed index combines an extremely compact representation along with very high throughput. Besides, its construction from the de Bruijn graph sequences is efficient and does not need supplementary memory. The efficient index implementation achieves to index the k-mers from the human genome with 8GB within 10 minutes and can scale up to the large axolotl genome with 63 GB within 76 minutes. Furthermore, while being memory efficient, the index allows above a million queries per second on a single CPU in our experiments, and the use of multiple cores raises its throughput. Finally, we also present how the index can practically represent metagenomic and transcriptomic sequencing data to highlight its wide applicative range.AvailabilityThe index is implemented as a C++ library, is open source under AGPL3 license, and available at github.com/Malfoy/Blight. It is designed as a user-friendly library and comes along with samples code usage.

scholarly journals ANGSD-wrapper: utilities for analyzing next generation sequencing data

2016 ◽  
Author(s):  
Arun Durvasula ◽  
Paul J Hoffman ◽  
Tyler V Kent ◽  
Chaochih Liu ◽  
Thomas J Y Kono ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Molecular Ecology ◽  
Principal Component ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
User Friendly

High throughput sequencing has changed many aspects of population genetics, molecular ecology, and related fields, affecting both experimental design and data analysis. The software package ANGSD allows users to perform a number of population genetic analyses on high-throughput sequencing data. ANGSD uses probabilistic approaches to calculate genome-wide descriptive statistics. The package makes use of genotype likelihood estimates rather than SNP calls and is specifically designed to produce more accurate results for samples with low sequencing depth. ANGSD makes use of full genome data while handling a wide array of sampling and experimental designs. Here we present ANGSD-wrapper, a set of wrapper scripts that provide a user-friendly interface for running ANGSD and visualizing results. ANGSD-wrapper supports multiple types of analyses including esti- mates of nucleotide sequence diversity and performing neutrality tests, principal component analysis, estimation of admixture proportions for individuals samples, and calculation of statistics that quantify recent introgression. ANGSD-wrapper also provides interactive graphing of ANGSD results to enhance data exploration. We demonstrate the usefulness of ANGSD-wrapper by analyzing resequencing data from populations of wild and domesticated Zea. ANGSD-wrapper is freely available from https://github.com/mojaveazure/angsd-wrapper.

scholarly journals COSIFER: a Python package for the consensus inference of molecular interaction networks

2020 ◽  
Author(s):  
Matteo Manica ◽  
Charlotte Bunne ◽  
Roland Mathis ◽  
Joris Cadow ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
High Throughput ◽  
Network Inference ◽  
State Of The Art ◽  
Interaction Network ◽  
Molecular Networks ◽  
Supplementary Information ◽  
Web Based ◽  
Molecular Interaction Networks ◽  
Inference Methods ◽  
User Friendly

Abstract Summary The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. Availability and implementation COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ORFik: a comprehensive R toolkit for the analysis of translation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Håkon Tjeldnes ◽  
Kornel Labun ◽  
Yamila Torres Cleuren ◽  
Katarzyna Chyżyńska ◽  
Michał Świrski ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Sequencing Data ◽  
Protein Coding ◽  
High Throughput Sequencing Data ◽  
Upstream Open Reading Frames ◽  
Many Core ◽  
User Friendly

Abstract Background With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays. Results Here, we introduce ORFik, a user-friendly R/Bioconductor API and toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5′UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames (uORFs). As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5′ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions. Conclusion In summary, ORFik introduces hundreds of tested, documented and optimized methods. ORFik is designed to be easily customizable, enabling users to create complete workflows from raw data to publication-ready figures for several types of sequencing data. Finally, by improving speed and scope of many core Bioconductor functions, ORFik offers enhancement benefiting the entire Bioconductor environment. Availability http://bioconductor.org/packages/ORFik.

scholarly journals CloudDOE: A User-Friendly Tool for Deploying Hadoop Clouds and Analyzing High-Throughput Sequencing Data with MapReduce

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e98146 ◽  
Author(s):  
Wei-Chun Chung ◽  
Chien-Chih Chen ◽  
Jan-Ming Ho ◽  
Chung-Yen Lin ◽  
Wen-Lian Hsu ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
User Friendly

scholarly journals ANGSD-wrapper: utilities for analyzing next generation sequencing data

2016 ◽  
Author(s):  
Arun Durvasula ◽  
Paul J Hoffman ◽  
Tyler V Kent ◽  
Chaochih Liu ◽  
Thomas J Y Kono ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Molecular Ecology ◽  
Principal Component ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
User Friendly

High throughput sequencing has changed many aspects of population genetics, molecular ecology, and related fields, affecting both experimental design and data analysis. The software package ANGSD allows users to perform a number of population genetic analyses on high-throughput sequencing data. ANGSD uses probabilistic approaches to calculate genome-wide descriptive statistics. The package makes use of genotype likelihood estimates rather than SNP calls and is specifically designed to produce more accurate results for samples with low sequencing depth. ANGSD makes use of full genome data while handling a wide array of sampling and experimental designs. Here we present ANGSD-wrapper, a set of wrapper scripts that provide a user-friendly interface for running ANGSD and visualizing results. ANGSD-wrapper supports multiple types of analyses including esti- mates of nucleotide sequence diversity and performing neutrality tests, principal component analysis, estimation of admixture proportions for individuals samples, and calculation of statistics that quantify recent introgression. ANGSD-wrapper also provides interactive graphing of ANGSD results to enhance data exploration. We demonstrate the usefulness of ANGSD-wrapper by analyzing resequencing data from populations of wild and domesticated Zea. ANGSD-wrapper is freely available from https://github.com/mojaveazure/angsd-wrapper.

scholarly journals ORFik: a comprehensive R toolkit for the analysis of translation

2021 ◽  
Author(s):  
Håkon Tjeldnes ◽  
Kornel Labun ◽  
Yamila Torres Cleuren ◽  
Katarzyna Chyżyńska ◽  
Michał Świrski ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Sequencing Data ◽  
Protein Coding ◽  
Coding Regions ◽  
High Throughput Sequencing Data ◽  
Upstream Open Reading Frames ◽  
User Friendly

ABSTRACT•BackgroundWith the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processing, analyzing, and characterizing data produced by these assays.•ResultsHere, we introduce ORFik, a user-friendly R/Bioconductor toolbox for studying translation and its regulation. It extends GenomicRanges from the genome to the transcriptome and implements a framework that integrates data from several sources. ORFik streamlines the steps to process, analyze, and visualize the different steps of translation with a particular focus on initiation and elongation. It accepts high-throughput sequencing data from ribosome profiling to quantify ribosome elongation or RCP-seq/TCP-seq to also quantify ribosome scanning. In addition, ORFik can use CAGE data to accurately determine 5’UTRs and RNA-seq for determining translation relative to RNA abundance. ORFik supports and calculates over 30 different translation-related features and metrics from the literature and can annotate translated regions such as proteins or upstream open reading frames. As a use-case, we demonstrate using ORFik to rapidly annotate the dynamics of 5’ UTRs across different tissues, detect their uORFs, and characterize their scanning and translation in the downstream protein-coding regions.•Availabilityhttp://bioconductor.org/packages/ORFik

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