scholarly journals Corrigendum to: HRIBO: high-throughput analysis of bacterial ribosome profiling data

2021 ◽  
Keyword(s):  
High Throughput ◽  
Ribosome Profiling ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Bacterial Ribosome

scholarly journals HRIBO - High-throughput analysis of bacterial ribosome profiling data

2020 ◽  
Author(s):  
Rick Gelhausen ◽  
Florian Heyl ◽  
Sarah L. Svensson ◽  
Kathrin Froschauer ◽  
Lydia Hadjeras ◽  
...  
Keyword(s):  
High Throughput ◽  
Functional Characterization ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Small Proteins ◽  
Bacterial Ribosome ◽  
Rna Fragments ◽  
A Cell

AbstractMotivationRibosome profiling (Ribo-seq) is a powerful approach based on ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (<=70 amino acids) that are recalcitrant to biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the analysis of Ribo-seq data from prokaryotes, nor are they focused on the discovery of unannotated open reading frames (ORFs) in bacteria.ResultsWe present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional features. This facilitates the rapid discovery of novel ORFs and their prioritization for functional characterization.AvailabilityHRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO

scholarly journals HRIBO: high-throughput analysis of bacterial ribosome profiling data

2020 ◽  
Author(s):  
Rick Gelhausen ◽  
Sarah L Svensson ◽  
Kathrin Froschauer ◽  
Florian Heyl ◽  
Lydia Hadjeras ◽  
...  
Keyword(s):  
High Throughput ◽  
Functional Characterization ◽  
Ribosome Profiling ◽  
Open Reading Frames ◽  
Cdna Libraries ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Small Proteins ◽  
Rna Fragments ◽  
A Cell

Abstract Motivation Ribosome profiling (Ribo-seq) is a powerful approach based on deep sequencing of cDNA libraries generated from ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (50–100 amino acids) that are recalcitrant to many standard biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the automatic processing and analysis of data from bacteria, nor are they focused on the discovery of unannotated open reading frames (ORFs). Results We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional feature information and expression values. This facilitates the rapid and high-confidence discovery of novel ORFs and their prioritization for functional characterization. Availabilityand implementation HRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO.

Combination of Free Software, Light Microscopy and Non-specific Dyes for a High-throughput Analysis in Live/Dead Cell Count

2015 ◽  
Vol 11 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Luciano Cardoso ◽  
Suellen Cordeiro ◽  
Marcio Fronza ◽  
Denise Endringer ◽  
Tadeu de Andrade ◽  
...  
Keyword(s):  
Light Microscopy ◽  
Cell Count ◽  
High Throughput ◽  
Free Software ◽  
Dead Cell ◽  
High Throughput Analysis ◽  
Throughput Analysis

Multiwell Combinatorial Hydrogel Array for High-Throughput Analysis of Cell–ECM Interactions

2021 ◽  
Author(s):  
Ruoxing Lei ◽  
Erin A. Akins ◽  
Kelly C. Y. Wong ◽  
Nicole A. Repina ◽  
Kayla J. Wolf ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Analysis ◽  
Throughput Analysis

Development of pattern recognition based on the nanosheet-DNA probes and an extendible DNA library

The Analyst ◽  
2021 ◽  
Author(s):  
Jiawei Qi ◽  
Pinhua Rao ◽  
Lele Wang ◽  
Li Xu ◽  
Yanli Wen ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
High Throughput ◽  
Analysis Data ◽  
Dna Probes ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Dna Library ◽  
Array Sensing

Pattern recognition, also called “array sensing” is a recognition strategy with a wide and expandable analysis range, based on the high-throughput analysis data. In this work, we constructed a sensor...

Highly Sensitive Detection of Paraquat with Pillar[5]arene as an aptamer in α-Hemolysin Nanopore

2021 ◽  
Author(s):  
Xiaojia Jiang ◽  
Mingsong Zang ◽  
Fei Li ◽  
Chunxi Hou ◽  
Quan Luo ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Single Molecule ◽  
Single Molecule Detection ◽  
Sensitive Detection ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
The Real ◽  
Highly Sensitive ◽  
Highly Sensitive Detection

Biological nanopore-based techniques have attracted more and more attention recently in the field of single-molecule detection, because they allow the real-time, sensitive, high-throughput analysis. Herein, we report an engineered biological...

scholarly journals Software Tools for High-Throughput Analysis and Archiving of Immunohistochemistry Staining Data Obtained with Tissue Microarrays

2002 ◽  
Vol 161 (5) ◽  
pp. 1557-1565 ◽  
Author(s):  
Chih Long Liu ◽  
Wijan Prapong ◽  
Yasodha Natkunam ◽  
Ash Alizadeh ◽  
Kelli Montgomery ◽  
...  
Keyword(s):  
High Throughput ◽  
Tissue Microarrays ◽  
Software Tools ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Immunohistochemistry Staining
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