HiTRACE 2.2.0 / HiTRACE-Web – High-Throughput Robust Analysis for Capillary Electrophoresis

HiTRACE 2.2.0 / HiTRACE-Web

:: DESCRIPTION

HiTRACE is a suite of robust and efficient analysis software to facilitate the analysis of large-scale high-throughput CE data.It has been intensively used for quantitating data for RNA and DNA based on the mutate-and-map methodology, chromatin footprinting, and other high-throughput structure mapping techniques.

HiTRACE-Web is an online version of HiTRACE that presents both standard features previously available only in the MATLAB environment as well as additional features such as automated band annotation and flexible adjustment of annotations, all via an integrative, user-friendly, and interactive environment.

::DEVELOPER

Das Lab , Seoul National University.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  MacOsX/ Windows
  • MatLab

:: DOWNLOAD

 HiTRACE

:: MORE INFORMATION

Citation

HiTRACE-Web: an online tool for robust analysis of high-throughput capillary electrophoresis.
Kim H, Cordero P, Das R, Yoon S.
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W492-8. doi: 10.1093/nar/gkt501.

HiTRACE: high-throughput robust analysis for capillary electrophoresis.
Yoon S, Kim J, Hum J, Kim H, Park S, Kladwang W, Das R.
Bioinformatics. 2011 Jul 1;27(13):1798-805. doi: 10.1093/bioinformatics/btr277

Hammer 0.2 – Error-correction of High-throughput Sequencing Datasets

Hammer 0.2

:: DESCRIPTION

Hammer is a tool for error correction of short read datasets with non-uniform coverage, such as single-cell data. In particular, Hammer does not make any uniformity assumptions on the distribution of the reads along the genome. It is based on a combination of the Hamming graph build from the set of k-mers and a simple probabilistic model for sequencing errors

::DEVELOPER

Medvedev Group

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

 Hammer

:: MORE INFORMATION

Citation

Medvedev, P., Scott, E., Kakaradov, B., Pevzner, P.,
Error correction of high-throughput sequencing datasets with non-uniform coverage,
Bioinformatics (2011) 27 (13): i137-i141.

Coev2Net – Boosting Confidence in High-throughput Protein-protein Interaction datasets

Coev2Net

:: DESCRIPTION

Coev2Net is a general framework to predict, assess and boost confidence in individual interactions inferred from a HTP experiment. For every pair of interaction in the HTP screen, Coev2Net provides a score to assess their likelihood of being co-evolved from interacting homologous sequences.

::DEVELOPER

Bonnie Berger‘s group at MIT.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Perl

:: DOWNLOAD

 Coev2Net

:: MORE INFORMATION

Citation:

Genome Biol. 2012 Aug 31;13(8):R76.
A computational framework for boosting confidence in high-throughput protein-protein interaction datasets.
Hosur R, Peng J, Vinayagam A, Stelzl U, Xu J, Perrimon N, Bienkowska J, Berger B.

miRModule – Identification of miRNA modules from high-throughput miRNA-mRNA Binding Experiments

miRModule

:: DESCRIPTION

MiRModule is a software tool for systematic discovery of miRNA modules from a set of predefined miRNA target sites. Given a sets of miRNA binding sites, miRModule efficiently identifies groups of miRNAs, whose binding sites significantly co-occur in the same set of target mRNAs, as putative miRNA modules. It works for both experimentally determined miRNA-mRNA binding sites (e.g. from CLASH) and computationally predicted miRNA-mRNA binding sites (e.g. from miRanda).

::DEVELOPER

Hu Lab – Data Integration and Knowledge Discovery @ UCF

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux

:: DOWNLOAD

 MiRModule

:: MORE INFORMATION

Citation

MicroRNA modules prefer to bind weak and unconventional target sites.
Ding J, Li X, Hu H.
Bioinformatics. 2014 Dec 18. pii: btu833.

QiSampler 20111215 – Evaluate Prioritization Done by Scoring Schemes and Experimental Parameters of High Throughput Biological Datasets

QiSampler 20111215

:: DESCRIPTION

QiSampler evaluates the prioritization done by experimental parameters of high throughput biological datasets.

::DEVELOPER

Computational Biology and Data Mining (CBDM) Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 QiSampler

:: MORE INFORMATION

Citation

BMC Res Notes. 2011 Mar 9;4:57.
QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards.
Fontaine JF, Suter B, Andrade-Navarro MA.

 

ESpritz 1.3 – Disorder Predictor for High-throughput Application

ESpritz 1.3

:: DESCRIPTION

Espritz is a new web server to detect regions of proteins which are thought to contain no structural content. This non-structure is known as protein disorder. Espritz uses an efficient and accurate prediction algorithm based on Bi-directional Recursive Neural networks (BRNN’s). BRNN’s are a sequence based machine learning algorithm which have being found to be useful for other structural predictions

::DEVELOPER

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 ESpritz

:: MORE INFORMATION

Citation:

ESpritz: accurate and fast prediction of protein disorder.
Walsh I, Martin AJ, Di Domenico T, Tosatto SC.
Bioinformatics. 2012 Feb 15;28(4):503-9. Epub 2011 Dec 20.

IsoEM 2.0.0 – Inferring Alternative Splicing Isoform Frequencies from High-Throughput RNA-Seq Data

IsoEM 2.0.0

:: DESCRIPTION

IsoEM package can be used to infer isoform and gene expression levels from high-throughput transcriptome sequencing (RNA-Seq) data. IsoEM uses a novel expectation-maximization algorithm that exploits read disambiguation information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand, and read pairing information (if available). Empirical experiments on synthetic datasets show that the algorithm significantly outperforms existing methods of isoform and gene expression level estimation from RNA-Seq data

::DEVELOPER

Bioinformatics Lab , Computer Science & Engineering Dept. University of Connecticut

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Java

:: DOWNLOAD

IsoEM

:: MORE INFORMATION

Citation:

M. Nicolae and S. Mangul and I.I. Mandoiu and A. Zelikovsky,
Estimation of alternative splicing isoform frequencies from RNA-Seq data,
Algorithms for Molecular Biology , pp. to appear, 2011

EMPeror 1.0.0-beta.20 – Analysis of High Throughput Microbial Ecology datasets

EMPeror 1.0.0-beta.20

:: DESCRIPTION

Emperor is an interactive next generation tool for the analysis, visualization and understanding of high throughput microbial ecology datasets.

::DEVELOPER

Knight Lab

:: SCREENSHOTS

 EMPeror

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python
  • Numpy
  • qcli

:: DOWNLOAD

 EMPeror

 :: MORE INFORMATION

Citation

EMPeror: a tool for visualizing high-throughput microbial community data.
Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R.
Gigascience. 2013 Nov 26;2(1):16. doi: 10.1186/2047-217X-2-16.

SbacHTS v5 – Spatial background correction for High-Throughput RNAi Screening

SbacHTS v5

:: DESCRIPTION

SbacHTS is a software for visualization, estimation and correction of spatial background noises of RNAi screening experiment results.

::DEVELOPER

The Quantitative Biomedical Research Center 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

 SbacHTS

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 1;29(17):2218-20. doi: 10.1093/bioinformatics/btt358. Epub 2013 Jun 28.
SbacHTS: spatial background noise correction for high-throughput RNAi screening.
Zhong R1, Kim MS, White MA, Xie Y, Xiao G.

Piranha 1.2.1 – Peak-caller for CLIP- and RIP-Seq high-throughput Protein-RNA interaction data

Piranha 1.2.1

:: DESCRIPTION

Piranha is a peak-caller for CLIP- and RIP-Seq high-throughput protein-RNA interaction data. It accepts input in BED or BAM format and identifies regions of significant enrichment for reads. Piranha can also optionally incorporate additional external covariates into the peak-calling process, and identify sites of differential binding occupancy between cell types, conditions or development stages.

::DEVELOPER

The Smith Lab 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • GCC

:: DOWNLOAD

 Piranha

:: MORE INFORMATION

Citation:

Bioinformatics. 2012 Dec 1;28(23):3013-20. doi: 10.1093/bioinformatics/bts569. Epub 2012 Sep 28.
Site identification in high-throughput RNA-protein interaction data.
Uren PJ, Bahrami-Samani E, Burns SC, Qiao M, Karginov FV, Hodges E, Hannon GJ, Sanford JR, Penalva LO, Smith AD.