Tag: DNA

Sequencher 5.4.6 – DNA Sequence Assembly and Analysis

Sequencher 5.4.6

:: DESCRIPTION

Sequencher is the industry standard software for DNA sequence analysis. It works with all automated sequencers and is widely known for its lightning-fast contig assembly, short learning curve, user-friendly editing tools, and superb technical support. First released almost 15 years ago, Sequencher is currently used for sequence analysis tasks in every major genomic and pharmaceutical company as well as numerous academic and government labs in over 40 countries around the world. Life Science researchers use Sequencher for many diverse DNA sequence analysis applications including de novo gene sequencing, mutation detection, forensic human identification, systematics, and more.

::DEVELOPER

Gene Codes Corporation

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOsX

:: DOWNLOAD

Sequencher Demo

:: MORE INFORMATION

InfoTrim – DNA Read Quality Trimmer using Entropy Created with Python

InfoTrim

:: DESCRIPTION

InfoTrim is a DNA read quality trimmer based on the Trimmomatic maximum information criterion model.

::DEVELOPER

Professor Zhang Liqing’s Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

InfoTrim

:: MORE INFORMATION

Citation

InfoTrim: A DNA read quality trimmer using entropy
2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

NanoMark – DNA Assembly Benchmark for Nanopore long reads

NanoMark

:: DESCRIPTION

NanoMark is a system for benchmarking DNA assembly tools, based on 3rd generation sequencers.

::DEVELOPER

NanoMark team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 NanoMark

:: MORE INFORMATION

Citation

Evaluation of hybrid and non-hybrid methods for de novo assembly of nanopore reads.
Sović I, Križanović K, Skala K, Šikić M.
Bioinformatics. 2016 May 9. pii: btw237.

BindUP 1.0 – Predicting DNA and RNA Binding Proteins using Electrostatic Patches

BindUP 1.0

:: DESCRIPTION

BindUP is an automatic server to predict DNA and RNA binding proteins given the three dimensional structure of the protein.

::DEVELOPER

the Mandel-Gutfreund Lab, at the Technion.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins.
Paz I, Kligun E, Bengad B, Mandel-Gutfreund Y.
Nucleic Acids Res. 2016 May 19. pii: gkw454.

DBSI – DNA Binding Site Predictor

DBSI

:: DESCRIPTION

DBSI (DNA Binding Site Identifier), is a binding site predictor for DNA, and it has also successfully identified binding sites for RNA and heparin.

::DEVELOPER

Mitchell Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 DBSI

:: MORE INFORMATION

Citation

DBSI Server: DNA Binding Site Identifier.
Sukumar S, Zhu X, Ericksen SS, Mitchell JC.
Bioinformatics. 2016 Jun 3. pii: btw315.

FadE 0.0.2 – DNA Methylation Detection

FadE 0.0.2

:: DESCRIPTION

FadE is a software package which was designed to determine the methylation parameter at each cytosine or cytosine-guanine position in the human genome. FadE uses color reads produced by the SOLiD sequencer.

::DEVELOPER

Ting Chen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 FadE

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jan 7;41(1):e14. doi: 10.1093/nar/gks830. Epub 2012 Sep 10.
FadE: whole genome methylation analysis for multiple sequencing platforms.
Souaiaia T1, Zhang Z, Chen T.

FiToM / xFITOM / jFITOM 201605 – Detection of Binding Sites in DNA or RNA Sequences

FiToM / xFITOM / jFITOM 201605

:: DESCRIPTION

FITOM is a computer program for the detection of binding sites in DNA or RNA sequences. It implements several methods described in the literature to compute an approximation of binding affinity for a particular site based on a collection of binding sequences provided by the user.

xFITOM is a fully featured GUI-enabled version of FITOM for Ms-Windows platforms that integrates additional functionality. The program provides an easy to use graphical user interface (GUI) to define all the relevant options for locating binding sites in genetic sequences.

jFITOM is a Java version of FITOM.

::DEVELOPER

the Erill Lab

:: SCREENSHOTS

xFITOM

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • C++ Compiler

:: DOWNLOAD

FITOM , xFITOM , jFITOM

:: MORE INFORMATION

Citation

Erill, I; O’Neill, M.C.
“A reexamination of information theory-based methods for DNA-binding site identification”
BMC Bioinformatics. 2009 Feb 11;10(1):57

Bhargava, N. & Erill, I. (2010)
“xFITOM: a generic GUI tool to search for transcription factor binding sites”,
Bioinformation 5(2) 49-51

methylKit 0.99.2 – R package for DNA methylation analysis

methylKit 0.99.2

:: DESCRIPTION

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. It can potentially handle whole-genome bisulfite sequencing data if proper input format is provided.

::DEVELOPER

BIMSB bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 methylKit

:: MORE INFORMATION

Citaton

Genome Biol. 2012 Oct 3;13(10):R87. [Epub ahead of print]
methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles.
Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, Mason CE.

HattCI 200160218 – Identification of attC sites in large DNA data sets

HattCI 200160218

:: DESCRIPTION

HattCI is a C-program for the identification of attC sites in any type of DNA data. It uses a hidden Markov model (HMM) to describe each part of the attC site in a probabilistic manner.

::DEVELOPER

HattCI team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  HattCI

:: MORE INFORMATION

Citation:

HattCI: Fast and Accurate attC site Identification Using Hidden Markov Models.
Pereira MB, Wallroth M, Kristiansson E, Axelson-Fisk M.
J Comput Biol. 2016 Jul 18.