Integrated design, execution, and analysis of arrayed and pooled CRISPR genome editing experiments

ABSTRACTCRISPR genome editing experiments offer enormous potential for the evaluation of genomic loci using arrayed single guide RNAs (sgRNAs) or pooled sgRNA libraries. Numerous computational tools are available to help design sgRNAs with optimal on-target efficiency and minimal off-target potential. In addition, computational tools have been developed to analyze deep sequencing data resulting from genome editing experiments. However, these tools are typically developed in isolation and oftentimes not readily translatable into laboratory-based experiments. Here we present a protocol that describes in detail both the computational and benchtop implementation of an arrayed and/or pooled CRISPR genome editing experiment. This protocol provides instructions for sgRNA design with CRISPOR, experimental implementation, and analysis of the resulting high-throughput sequencing data with CRISPResso. This protocol allows for design and execution of arrayed and pooled CRISPR experiments in 4-5 weeks by non-experts as well as computational data analysis in 1-2 days that can be performed by both computational and non-computational biologists alike.

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ReSeq simulates realistic Illumina high-throughput sequencing data

10.1101/2020.07.17.209072 ◽

2020 ◽

Author(s):

Stephan Schmeing ◽

Mark D. Robinson

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Synthetic Data ◽

Real Data ◽

Performance Evaluations ◽

Sequencing Data ◽

Scientific Result ◽

Computational Tools ◽

High Throughput Sequencing Data ◽

Coverage Model

AbstractIn high-throughput sequencing data, performance comparisons between computational tools are essential for making informed decisions in the data processing from raw data to the scientific result. Simulations are a critical part of method comparisons, but for standard Illumina sequencing of genomic DNA, they are often oversimplified, which leads to optimistic results for most tools.ReSeq improves the authenticity of synthetic data by extracting and reproducing key components from real data. Major advancements are the inclusion of systematic errors, a fragment-based coverage model and sampling-matrix estimates based on two-dimensional margins. These improvements lead to a better representation of the original k-mer spectrum and more faithful performance evaluations. ReSeq and all of its code are available at: https://github.com/schmeing/ReSeq

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ReSeq simulates realistic Illumina high-throughput sequencing data

Genome Biology ◽

10.1186/s13059-021-02265-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Stephan Schmeing ◽

Mark D. Robinson

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Synthetic Data ◽

Real Data ◽

Systematic Errors ◽

Performance Evaluations ◽

Sequencing Data ◽

Computational Tools ◽

High Throughput Sequencing Data ◽

Coverage Model

AbstractIn high-throughput sequencing data, performance comparisons between computational tools are essential for making informed decisions at each step of a project. Simulations are a critical part of method comparisons, but for standard Illumina sequencing of genomic DNA, they are often oversimplified, which leads to optimistic results for most tools. ReSeq improves the authenticity of synthetic data by extracting and reproducing key components from real data. Major advancements are the inclusion of systematic errors, a fragment-based coverage model and sampling-matrix estimates based on two-dimensional margins. These improvements lead to more faithful performance evaluations. ReSeq is available at https://github.com/schmeing/ReSeq.

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Faculty Opinions recommendation of Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726132071.793531014 ◽

2017 ◽

Author(s):

Sarah Rowland-Jones ◽

Sophie Andrews

Keyword(s):

Hiv Infection ◽

High Throughput ◽

High Throughput Sequencing ◽

Sequencing Data ◽

High Throughput Sequencing Data

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BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Bioinformatics ◽

10.1093/bioinformatics/btu010 ◽

2014 ◽

Vol 30 (9) ◽

pp. 1214-1219 ◽

Cited By ~ 6

Author(s):

C. Ye ◽

C. Hsiao ◽

H. Corrada Bravo

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Blind Deconvolution ◽

Sequencing Data ◽

Base Calling ◽

High Throughput Sequencing Data

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Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

Journal of Animal Science ◽

10.1093/jas/skab018 ◽

2021 ◽

Vol 99 (2) ◽

Author(s):

Yuhua Fu ◽

Pengyu Fan ◽

Lu Wang ◽

Ziqiang Shu ◽

Shilin Zhu ◽

...

Keyword(s):

High Throughput Sequencing ◽

Target Genes ◽

Target Prediction ◽

Large Data ◽

Sequencing Data ◽

Regulate Gene Expression ◽

High Throughput Sequencing Data ◽

Annotation Information ◽

Public Data ◽

Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

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deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

Nucleic Acids Research ◽

10.1093/nar/gkaa1039 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D877-D883

Author(s):

Fangzhou Xie ◽

Shurong Liu ◽

Junhao Wang ◽

Jiajia Xuan ◽

Xiaoqin Zhang ◽

...

Keyword(s):

High Throughput Sequencing ◽

Clinical Information ◽

Sequencing Data ◽

Normal Tissues ◽

Interactive Analysis ◽

High Throughput Sequencing Data ◽

Expression Atlas ◽

Expression Evolution ◽

Noninvasive Biomarkers ◽

Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

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Improving gene function predictions using independent transcriptional components

Nature Communications ◽

10.1038/s41467-021-21671-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Carlos G. Urzúa-Traslaviña ◽

Vincent C. Leeuwenburgh ◽

Arkajyoti Bhattacharya ◽

Stefan Loipfinger ◽

Marcel A. T. M. van Vugt ◽

...

Keyword(s):

Independent Component Analysis ◽

High Throughput Sequencing ◽

Principal Component ◽

Component Analysis ◽

Independent Component ◽

Sequencing Data ◽

New Members ◽

High Throughput Sequencing Data ◽

Gene Sets ◽

Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

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Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽

10.3897/mycokeys.39.28109 ◽

2018 ◽

Vol 39 ◽

pp. 29-40 ◽

Cited By ~ 21

Author(s):

Sten Anslan ◽

R. Henrik Nilsson ◽

Christian Wurzbacher ◽

Petr Baldrian ◽

Leho Tedersoo ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Computation Time ◽

Potential Effect ◽

Data Sets ◽

Sequencing Data ◽

Operational Taxonomic Units ◽

High Throughput Sequencing Data ◽

Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

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circtools—a one-stop software solution for circular RNA research

Bioinformatics ◽

10.1093/bioinformatics/bty948 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2326-2328 ◽

Cited By ~ 13

Author(s):

Tobias Jakobi ◽

Alexey Uvarovskii ◽

Christoph Dieterich

Keyword(s):

High Throughput Sequencing ◽

Circular Rna ◽

Statistical Testing ◽

Supplementary Information ◽

Circular Rnas ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Multi Stage ◽

Sequence Reconstruction ◽

One Stop

Abstract Motivation Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. Results Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. Availability and implementation circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. Supplementary information Supplementary data are available at Bioinformatics online.

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