scholarly journals clusterSeq: methods for identifying co-expression in high-throughput sequencing data

2017 ◽  
Author(s):  
Thomas J. Hardcastle ◽  
Irene Papatheodorou
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Structural Elements ◽  
Sequencing Data ◽  
Experimental Conditions ◽  
Functional Relationships ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Novel Approach ◽  
Significant Step

ABSTRACTSummary:Identifying gene co-expression is a significant step in understanding functional relationships between genes. Existing methods primarily depend on analyses of correlation between pairs of genes; however, this neglects structural elements between experimental conditions. We present a novel approach to identifying clusters of co-expressed genes that incorporates these structures.Availability:The methods are released on Bioconductor as the clusterSeq package (https://bioconductor.org/packages/release/bioc/html/clusterSeq.html).Contact: [email protected]

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2209 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

Summary.In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available athttp://fluff.readthedocs.org.Availability.fluff is implemented in Python and runs on Linux. The source code is freely available for download athttps://github.com/simonvh/fluff.

scholarly journals Methods for discovering genomic loci exhibiting complex patterns of differential methylation

2015 ◽  
Author(s):  
Thomas J Hardcastle
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Cytosine Methylation ◽  
Differential Methylation ◽  
Sequencing Data ◽  
Experimental Conditions ◽  
Sodium Bisulphite ◽  
Regulatory Pathways ◽  
High Throughput Sequencing Data ◽  
Biological Replication

AbstractCytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation ‘loci’; contiguous regions of methylation consistently methylated across biological replicates. The mapping of these loci allows for associations with other genomic factors to be identified, and for analyses of differential methylation to take place.The segmentSeq R package is extended to identify methylation loci from high-throughput sequencing data from multiple experimental conditions. A statistical model is then developed that accounts for biological replication and variable rates of non-conversion of cytosines in each sample to compute posterior likelihoods of methylation at each locus within an empirical Bayesian framework. The same model is used as a basis for analysis of differential methylation between multiple experimental conditions with the baySeq R package. We demonstrate this method through an analysis of data derived from Dicer-like mutants in Arabidopsis that reveals complex interactions between the different Dicer-like mutants and their methylation pathways. We also show in simulation studies that this approach can be significantly more powerful in the detection of differential methylation than existing methods.

scholarly journals Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Enrique Blanco ◽  
Mar González-Ramírez ◽  
Luciano Di Croce
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Graphical Analysis ◽  
Sequencing Data ◽  
Efficient Manner ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Almost All ◽  
User Friendly

AbstractLarge-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

2016 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

AbstractSummaryIn this application note we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available at http://fluff.readthedocs.org.Availabilityfluff is implemented in Python and runs on Linux. The source code is freely available for download at http://github.com/simonvh/[email protected]

scholarly journals HTSSIP: an R package for analysis of high throughput sequencing data from nucleic acid stable isotope probing (SIP) experiments

2017 ◽  
Author(s):  
Nicholas D. Youngblut ◽  
Samuel E. Barnett ◽  
Daniel H. Buckley
Keyword(s):  
High Resolution ◽  
Stable Isotope ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Stable Isotope Probing ◽  
Sequencing Data ◽  
Metabolic Processes ◽  
Link Type ◽  
High Throughput Sequencing Data

AbstractCombining high throughput sequencing with stable isotope probing (HTS-SIP) is a powerful method for mapping in situ metabolic processes to thousands of microbial taxa. However, accurately mapping metabolic processes to taxa is complex and challenging. Multiple HTS-SIP data analysis methods have been developed, including high-resolution stable isotope probing (HR-SIP), multi-window high-resolution stable isotope probing (MW-HR-SIP), quantitative stable isotope probing (q-SIP), and ΔBD. Currently, the computational tools to perform these analyses are either not publicly available or lack documentation, testing, and developer support. To address this shortfall, we have developed the HTSSIP R package, a toolset for conducting HTS-SIP analyses in a straightforward and easily reproducible manner. The HTSSIP package, along with full documentation and examples, is available from CRAN at https://cran.r-project.org/web/packages/HTSSIP/index.html and Github at https://github.com/nick-youngblut/HTSSIP.

scholarly journals ARSDA: A new approach for storing, transmitting and analyzing high-throughput sequencing data

2017 ◽  
Author(s):  
Xuhua Xia
Keyword(s):  
Bacillus Subtilis ◽  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Plain Text ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Fastq Format ◽  
Single Entry

ABSTRACTTwo major stumbling blocks exist in high-throughput sequencing (HTS) data analysis. The first is the sheer file size typically in gigabytes when uncompressed, causing problems in storage, transmission and analysis. However, these files do not need to be so large and can be reduced without loss of information. Each HTS file, either in compressed .SRA or plain text .fastq format, contains numerous identical reads stored as separate entries. For example, among 44603541 forward reads in the SRR4011234.sra file (from aBacillus subtilistranscriptomic study) deposited at NCBI’s SRA database, one read has 497027 identical copies. Instead of storing them as separate entries, one can and should store them as a single entry with the SeqID_NumCopy format (which I dub as FASTA+ format). The second is the proper allocation reads that map equally well to paralogous genes. I illustrate in detail a new method for such allocation. I have developed ARSDA software that implement these new approaches. A number of HTS files for model species are in the process of being processed and deposited athttp://coevol.rdc.uottawa.cato demonstrate that this approach not only saves a huge amount of storage space and transmission bandwidth, but also dramatically reduces time in downstream data analysis. Instead of matching the 497027 identical reads separately against theBacillus subtilisgenome, one only needs to match it once. ARSDA includes functions to take advantage of HTS data in the new sequence format for downstream data analysis such as gene expression characterization. ARSDA can be run on Windows, Linux and Macintosh computers and is freely available athttp://dambe.bio.uottawa.ca/ARSDA/ARSDA.aspx.

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

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
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.

scholarly journals Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Genomics ◽  
2017 ◽  
Vol 109 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Yan Guo ◽  
Yulin Dai ◽  
Hui Yu ◽  
Shilin Zhao ◽  
David C. Samuels ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Data Analysis
Sign in / Sign up

Export Citation Format

Share Document