scholarly journals Factorbook Motif Pipeline: A de novo motif discovery and filtering web server for ChIP-seq peaks

2015 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Jiali Zhuang ◽  
Jie Wang ◽  
Zhiping Weng
Keyword(s):  
Motif Discovery ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Statistical Tests ◽  
Web Server ◽  
Biological Processes ◽  
Web Based ◽  
Sequencing Technologies ◽  
De Novo Motif Discovery

Summary: High-throughput sequencing technologies such as ChIP-seq have deepened our understanding in many biological processes. De novo motif search is one of the key downstream computational analysis following the ChIP-seq experiments and several algorithms have been proposed for this purpose. However, most web-based systems do not perform independent filtering or enrichment analyses to ensure the quality of the discovered motifs. Here, we developed a web server Factorbook Motif Pipeline based on an algorithm used in analyzing ENCODE consortium ChIP-seq datasets. It performs comprehensive analysis on the set of peaks detected from a ChIP-seq experiments: (i) de novo motif discovery; (ii) independent composition and bias analyses and (iii) matching to the annotated motifs. The statistical tests employed in our pipeline provide a reliable measure of confidence as to how significant are the motifs reported in the discovery step. Availability: Factorbook Motif Pipeline source code is accessible through the following URL. https://github.com/joshuabhk/factorbook-motif-pipeline

scholarly journals AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX

2020 ◽  
Vol 36 (9) ◽  
pp. 2905-2906 ◽  
Author(s):  
Kevin R Shieh ◽  
Christina Kratschmer ◽  
Keith E Maier ◽  
John M Greally ◽  
Matthew Levy ◽  
...  
Keyword(s):  
Motif Discovery ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Rna Aptamers ◽  
Supplementary Information ◽  
Good Correspondence ◽  
Detection Algorithms ◽  
De Novo Motif Discovery ◽  
Exponential Enrichment ◽  
Analytical Approaches

Abstract Summary High-throughput sequencing can enhance the analysis of aptamer libraries generated by the Systematic Evolution of Ligands by EXponential enrichment. Robust analysis of the resulting sequenced rounds is best implemented by determining a ranked consensus of reads following the processing by multiple aptamer detection algorithms. While several such approaches have been developed to this end, their installation and implementation is problematic. We developed AptCompare, a cross-platform program that combines six of the most widely used analytical approaches for the identification of RNA aptamer motifs and uses a simple weighted ranking to order the candidate aptamers, all driven within the same GUI-enabled environment. We demonstrate AptCompare’s performance by identifying the top-ranked candidate aptamers from a previously published selection experiment in our laboratory, with follow-up bench assays demonstrating good correspondence between the sequences’ rankings and their binding affinities. Availability and implementation The source code and pre-built virtual machine images are freely available at https://bitbucket.org/shiehk/aptcompare. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The BaMM web server for de-novo motif discovery and regulatory sequence analysis

2018 ◽  
Vol 46 (W1) ◽  
pp. W215-W220 ◽  
Author(s):  
Anja Kiesel ◽  
Christian Roth ◽  
Wanwan Ge ◽  
Maximilian Wess ◽  
Markus Meier ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Motif Discovery ◽  
De Novo ◽  
Web Server ◽  
Regulatory Sequence ◽  
De Novo Motif Discovery

scholarly journals SCOPE: a web server for practical de novo motif discovery

2007 ◽  
Vol 35 (Web Server) ◽  
pp. W259-W264 ◽  
Author(s):  
J. M. Carlson ◽  
A. Chakravarty ◽  
C. E. DeZiel ◽  
R. H. Gross
Keyword(s):  
Motif Discovery ◽  
De Novo ◽  
Web Server ◽  
De Novo Motif Discovery

scholarly journals AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX

2018 ◽  
Author(s):  
Kevin R. Shieh ◽  
Christina Kratschmer ◽  
Keith E. Maier ◽  
John M. Greally ◽  
Matthew Levy ◽  
...  
Keyword(s):  
Motif Discovery ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Rna Aptamers ◽  
Good Correspondence ◽  
Detection Algorithms ◽  
De Novo Motif Discovery ◽  
Cross Platform ◽  
Exponential Enrichment ◽  
Analytical Approaches

ABSTRACTSummary:High-Throughput Sequencing can enhance the analysis of aptamer libraries generated by the Systematic Evolution of Ligands by EXponential enrichment (HTS-SELEX). Robust analysis of the resulting sequenced rounds is best implemented by determining a ranked consensus of reads following the processing by multiple aptamer detection algorithms. Whilst several such approaches have been developed to this end, their installation and implementation is problematic. We developed AptCompare, a cross-platform program that combines six of the most widely used analytical approaches for the identification of RNA aptamer motifs and uses a simple weighted ranking to order the candidate aptamers, all driven within the same GUI- enabled environment. We demonstrate AptCompare’s performance by identifying the top-ranked candidate aptamers from a previously published selection experiment in our laboratory, with follow-up bench assays demonstrating good correspondence between the sequences’ rankings and their binding affinities.Availability and Implementation:The source code and pre-built virtual machine images are freely available at https://bitbucket.org/shiehk/aptcompare.

scholarly journals TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Louis T. Dang ◽  
Markus Tondl ◽  
Man Ho H. Chiu ◽  
Jerico Revote ◽  
Benedict Paten ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Motif Discovery ◽  
De Novo ◽  
Next Generation ◽  
De Novo Motif Discovery ◽  
Generation Sequencing

De Novo Motif Prediction Using the Fireworks Algorithm

Biotechnology ◽  
2019 ◽  
pp. 1069-1085
Author(s):  
Andrei Lihu ◽  
Ștefan Holban
Keyword(s):  
Gene Expression ◽  
Motif Discovery ◽  
De Novo ◽  
Fireworks Algorithm ◽  
Regulatory Processes ◽  
Motif Prediction ◽  
Leibler Divergence ◽  
De Novo Motif Discovery ◽  
Low Performance ◽  
Motif Finding Algorithm

De novo motif discovery is essential in understanding the cis-regulatory processes that play a role in gene expression. Finding unknown patterns of unknown lengths in massive amounts of data has long been a major challenge in computational biology. Because algorithms for motif prediction have always suffered of low performance issues, there is a constant effort to find better techniques. Evolutionary methods, including swarm intelligence algorithms, have been applied with limited success for motif prediction. However, recently developed methods, such as the Fireworks Algorithm (FWA) which simulates the explosion process of fireworks, may show better prospects. This paper describes a motif finding algorithm based on FWA that maximizes the Kullback-Leibler divergence between candidate solutions and the background noise. Following the terminology of FWA's framework, the candidate motifs are fireworks that generate additional sparks (i.e. derived motifs) in their neighborhood. During the iterations, better sparks can replace the fireworks, as the Fireworks Motif Finder (FW-MF) assumes a one occurrence per sequence mode. The results obtained on a standard benchmark for promoter analysis show that our proof of concept is promising.

Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers

2020 ◽  
Author(s):  
Yuansheng Liu ◽  
Xiaocai Zhang ◽  
Quan Zou ◽  
Xiangxiang Zeng
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Complementary Strand ◽  
Short Reads ◽  
Sequencing Technologies ◽  
Computational Resources

Abstract Summary Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. Availability and implementation https://github.com/yuansliu/minirmd. Supplementary information Supplementary data are available at Bioinformatics online.

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