DNA Chisel, a versatile sequence optimizer

Abstract Motivation Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine. Results DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. Availability and implementation The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation). Supplementary information Supplementary data are available at Bioinformatics online.

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DNA Chisel, a versatile sequence optimizer

10.1101/2019.12.16.877480 ◽

2019 ◽

Author(s):

Valentin Zulkower ◽

Susan Rosser

Keyword(s):

Open Source ◽

Dna Sequence ◽

Web Application ◽

Optimization Problems ◽

Supplementary Information ◽

Sequence Optimization ◽

Sequence Design ◽

Optimization Framework ◽

Link Type ◽

Dna Sequence Design

AbstractMotivationAccounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine.ResultsDNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations.Availabilityas a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (code and documentation at https://github.com/Edinburgh-Genome-Foundry/DNAChisel)[email protected] informationattached.

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ATGme: Open-source web application for rare codon identification and custom DNA sequence optimization

BMC Bioinformatics ◽

10.1186/s12859-015-0743-5 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 12

Author(s):

Edward Daniel ◽

Goodluck U. Onwukwe ◽

Rik K. Wierenga ◽

Susan E. Quaggin ◽

Seppo J. Vainio ◽

...

Keyword(s):

Open Source ◽

Dna Sequence ◽

Web Application ◽

Rare Codon ◽

Sequence Optimization

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Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences

Bioinformatics ◽

10.1093/bioinformatics/btz144 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3826-3828 ◽

Cited By ~ 9

Author(s):

Kirill Kryukov ◽

Mahoko Takahashi Ueda ◽

So Nakagawa ◽

Tadashi Imanishi

Keyword(s):

Open Source ◽

Dna Sequence ◽

Compression Ratio ◽

Dna Sequences ◽

General Purpose ◽

Supplementary Information ◽

File Format ◽

Storage Space ◽

Supplementary Data ◽

Network Transmission

Abstract Summary DNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF)—a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. Nucleotide Archival Format compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli and zstd. Availability and implementation NAF compressor and decompressor, as well as format specification are available at https://github.com/KirillKryukov/naf. Format specification is in public domain. Compressor and decompressor are open source under the zlib/libpng license, free for nearly any use. Supplementary information Supplementary data are available at Bioinformatics online.

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ccNetViz: a WebGL-based JavaScript library for visualization of large networks

Bioinformatics ◽

10.1093/bioinformatics/btaa559 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4527-4529

Author(s):

Ales Saska ◽

David Tichy ◽

Robert Moore ◽

Achilles Rasquinha ◽

Caner Akdas ◽

...

Keyword(s):

Systems Biology ◽

Complex Networks ◽

Open Source ◽

High Speed ◽

A Priori ◽

Supplementary Information ◽

Network Visualization ◽

Supplementary Data ◽

Web Based ◽

Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

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Research on the DNA Sequence Design Based on GA/PSO Algorithms

2008 2nd International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2008.200 ◽

2008 ◽

Cited By ~ 10

Author(s):

Chunxia Xu ◽

Qiang Zhang ◽

Bin Wang ◽

Rui Zhang

Keyword(s):

Dna Sequence ◽

Sequence Design ◽

Dna Sequence Design

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YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Bioinformatics ◽

10.1093/bioinformatics/btz402 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4525-4527 ◽

Cited By ~ 10

Author(s):

Alex X Lu ◽

Taraneh Zarin ◽

Ian S Hsu ◽

Alan M Moses

Keyword(s):

Image Analysis ◽

Web Application ◽

Single Cells ◽

Yeast Cells ◽

Supplementary Information ◽

Supplementary Data ◽

User Friendly ◽

Microscopy Images

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

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Evolutionary Multi-Objective Optimization for DNA Sequence Design

Multi-Objective Optimization in Computational Intelligence ◽

10.4018/978-1-59904-498-9.ch009 ◽

2011 ◽

pp. 239-264

Author(s):

Soo-Yong Shin ◽

In-Hee Lee ◽

Byoung-Tak Zhang

Keyword(s):

Evolutionary Algorithms ◽

Dna Sequence ◽

Dna Sequences ◽

Dna Microarrays ◽

Superior Performance ◽

Final Decision ◽

Sequence Design ◽

Multi Objective ◽

Dna Sequence Design ◽

Np Problems

Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA nano-assembly, DNA microarrays and polymerase chain reaction. Sequence design involves a number of heterogeneous and conflicting design criteria. Also, it is proven as a class of NP problems. These suggest that multi-objective evolutionary algorithms (MOEAs) are actually good candidates for DNA sequence optimization. In addition, the characteristics of MOEAs including simple addition/deletion of objectives and easy incorporation of various existing tools and human knowledge into the final decision process could increase the reliability of final DNA sequence set. In this chapter, we review multi-objective evolutionary approaches to DNA sequence design. In particular, we analyze the performance of e-multi-objective evolutionary algorithms on three DNA sequence design problems and validate the results by showing superior performance to previous techniques.

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DrawGlycan-SNFG and gpAnnotate: rendering glycans and annotating glycopeptide mass spectra

Bioinformatics ◽

10.1093/bioinformatics/btz819 ◽

2019 ◽

Cited By ~ 4

Author(s):

Kai Cheng ◽

Gabrielle Pawlowski ◽

Xinheng Yu ◽

Yusen Zhou ◽

Sriram Neelamegham

Keyword(s):

Mass Spectrometry ◽

Open Source ◽

Mass Spectra ◽

Supplementary Information ◽

Supplementary Data ◽

International Union ◽

Open Source Program ◽

Source Program ◽

Wide Range ◽

Peptide Modifications

Abstract Summary This manuscript describes an open-source program, DrawGlycan-SNFG (version 2), that accepts IUPAC (International Union of Pure and Applied Chemist)-condensed inputs to render Symbol Nomenclature For Glycans (SNFG) drawings. A wide range of local and global options enable display of various glycan/peptide modifications including bond breakages, adducts, repeat structures, ambiguous identifications etc. These facilities make DrawGlycan-SNFG ideal for integration into various glycoinformatics software, including glycomics and glycoproteomics mass spectrometry (MS) applications. As a demonstration of such usage, we incorporated DrawGlycan-SNFG into gpAnnotate, a standalone application to score and annotate individual MS/MS glycopeptide spectrum in different fragmentation modes. Availability and implementation DrawGlycan-SNFG and gpAnnotate are platform independent. While originally coded using MATLAB, compiled packages are also provided to enable DrawGlycan-SNFG implementation in Python and Java. All programs are available from https://virtualglycome.org/drawglycan; https://virtualglycome.org/gpAnnotate. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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