Tag: Process

W-ChIPeaks – Process ChIP-chip and ChIP-seq data

W-ChIPeaks

:: DESCRIPTION

W-ChIPeaks employs a probe-based or bin-based enrichment threshold to define peaks and applies statistical methods to control the false discovery rate for identified peaks.

::DEVELOPER

Jin Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Feb 1;27(3):428-30. doi: 10.1093/bioinformatics/btq669. Epub 2010 Dec 7.
W-ChIPeaks: a comprehensive web application tool for processing ChIP-chip and ChIP-seq data.
Lan X, Bonneville R, Apostolos J, Wu W, Jin VX.

HTSeq 0.13.5 – Process and Analyze data from High-throughput Sequencing (HTS) Assays

HTSeq 0.13.5

:: DESCRIPTION

HTSeq is a Python package that provides infrastructure to process data from high-throughput sequencing assays.

::DEVELOPER

Huber Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 HTSeq

:: MORE INFORMATION

Citation

HTSeq–a Python framework to work with high-throughput sequencing data.
Anders S, Pyl PT, Huber W.
Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638.

KGML-ED 1.0 – Edit, Process, and Visualize KGML pathway files

KGML-ED 1.0

:: DESCRIPTION

KGML-ED supports the dynamic exploration and editing of KEGG Pathway diagrams. It is a graphical network editor, that provides read- and write-support for the KGML (KEGG Markup Language) file format. Pathway files are loaded and transformed into a graph network which may be modified to fulfill user-specific needs (e.g. it is possible, to delete or add network elements, change labels and colors). Novel network exploration approaches are supported by the system as well.

::DEVELOPER

KGML-ED team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux/  MacOSX
  • Java

:: DOWNLOAD

 KGML-ED

:: MORE INFORMATION

Citation

Christian Klukas and Falk Schreiber
Dynamic exploration and editing of KEGG pathway diagrams.
Bioinformatics 2007 23: 344-350.

CANGS 1.1 – Process & Anylyze 454 GS-FLX data in Biodiversity studies

CANGS 1.1

:: DESCRIPTION

CANGS (Cleaning and Analyzing Next Generation Sequences)  is a utility, which is designed to automate the process of trimming sequences, filtering low quality sequences and performing various analyses for diversity study. There are basically two layers in CANGS 1) processing layer and 2) Analysis Layer.

::DEVELOPER

Institute of Population Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CANGS

:: MORE INFORMATION

Citation:

CANGS: a user-friendly utility for processing and analyzing 454 GS-FLX data in biodiversity studies.
Pandey RV, Nolte V, Schlštterer C.
BMC Res Notes. 2010 Jan 11;3:3.

ValFold 1.0.0 – Program for the Aptamer Truncation Process

ValFold 1.0.0

:: DESCRIPTION

ValFold is the program for the aptamer truncation process.

::DEVELOPER

Joe Akitomi , VALWAY Technology Center, NEC Soft, Ltd. (E-mail: akitom-jou@mxf.nes.nec.co.jp)

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • Java 

:: DOWNLOAD

 ValFold

:: MORE INFORMATION

Citation:

Bioinformation. 2011;7(1):38-40. Epub 2011 Aug 20.
ValFold: Program for the aptamer truncation process.
Akitomi J, Kato S, Yoshida Y, Horii K, Furuichi M, Waga I.

Q-Gene 1.2 – Process Quantitative Real-time RT-PCR Data

Q-Gene 1.2

:: DESCRIPTION

Q-Gene is an application for the processing of quantitative real-time RT–PCR data. It offers the user the possibility to freely choose between two principally different procedures to calculate normalized gene expressions as either means of Normalized Expressions or Mean Normalized Expressions. In this contribution it will be shown that the calculation of Mean Normalized Expressions has to be used for processing simplex PCR data, while multiplex PCR data should preferably be processed by calculating Normalized Expressions. The two procedures, which are currently in widespread use and regarded as more or less equivalent alternatives, should therefore specifically be applied according to the quantification procedure used.

::DEVELOPER

Perikles Simon

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Microsoft Excel

:: DOWNLOAD

 Q-Gene

:: MORE INFORMATION

Citation

Simon P.
Q-Gene: processing quantitative real-time RT-PCR data.
Bioinformatics. 2003 Jul 22;19(11):1439-40.

TiMAT 3.4.4 – Process Chip-chip Tiling Array Experiments

TiMAT 3.4.4

:: DESCRIPTION

TiMAT is an open-source, Java based set of scripts used for processing chip-chip tiling array experiments. Its four main functionalities are (1) smooth noisy signals, (2) optionally calculate false discovery rates, (3) identify enriched intervals and (4) identify peaks within these enriched intervals. While parts of TiMAT might be used in analyzing expression arrays, other parts, such as the symmetric null-p calculations are not appropriate for such experiments– TiMAT is designed for chip-chip experiments.

::DEVELOPER

the Berkeley Drosophila Transcription Network Project,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Java

:: DOWNLOAD

 TiMAT

:: MORE INFORMATION

DPS 2.13 – Processing of Single Crystal X-ray Diffraction data of proteins etc

DPS 2.13

:: DESCRIPTION

The DPS (Data Processing Suite) will be a complete package for processing of single crystal X-ray diffraction data of proteins, viruses, nucleic acids and other large biological complexes with emphasis on data collected at synchrotron sources.

::DEVELOPER

Marian Szebenyi 

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 DPS

:: MORE INFORMATION

Citation:

Rossmann, M.G. & van Beek, C.G. (1999),
“Data processing“,
Acta Cryst. D55, 1631-1653.

CRC 1.1 – Dirichlet Process Model-based Cluster

CRC 1.1

:: DESCRIPTION

CRC (Chinese Restaurant Cluster) implements a model-based Bayesian clustering algorithm. The cluster assignment procedure can be regarded as following a iterative Chinese restaurant process. This program is designed to cluster microarray gene expression data collected from multiple experiments. missing data is allowed. The program is written in C++, and can be run under Linux, Unix, Windows, MAC OSX operating system as a command line exexutable. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. Here is some more details on why you should try CRC for your microarray data analysis.

CRC Online Version

::DEVELOPER

Steve Qin @ the Center for Statistical Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows / MacOsX

:: DOWNLOAD

 CRC

:: MORE INFORMATION

Citation

Qin ZS.
Clustering microarray gene expression data using weighted Chinese restaurant process.
Bioinformatics. 2006 Aug 15;22(16):1988-97. Epub 2006 Jun 9.

Exit mobile version