Tag: Gene Expression Level

RBSDesigner 1.0.78 – Design Synthetic Ribosome Binding Sites (RBS) to control Gene Expression levels

RBSDesigner 1.0.78

:: DEVELOPER

RBSDesigner was developed to computationally design synthetic ribosome binding sites (RBS) to control gene expression levels. Generally transcription processes are the major target for gene expression control, however, without considering translation processes the control could lead to unexpected expression results since translation efficiency is highly affected by nucleotide sequences nearby RBS such as coding sequences leading to distortion of RBS secondary structure.

:: DEVELOPER

Dokyun lab

:: SCREENSHOTS

RBSDesigner

:: REQUIREMENTS

  • Windows
  • Python
  • Perl
  • UNAFold

:: DOWNLOAD

RBSDesigner

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Oct 15;26(20):2633-4. doi: 10.1093/bioinformatics/btq458. Epub 2010 Aug 11.
RBSDesigner: software for designing synthetic ribosome binding sites that yields a desired level of protein expression.
Na D, Lee D.

Xpresso – Predicting Gene Expression levels from DNA Sequences

Xpresso

:: DESCRIPTION

Xpresso is a software suite whose goal is to predict gene expression levels and transcriptional activity from genomic sequences. It is trained using convolutional neural networks. Pre-trained models are available for the human, mouse, and several cell types for these species.

::DEVELOPER

Shendure Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows
  • Python
  • R

:: DOWNLOAD

Xpresso

:: MORE INFORMATION

Citation

Agarwal V, Shendure J.
Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks.
Cell Rep. 2020 May 19;31(7):107663. doi: 10.1016/j.celrep.2020.107663. PMID: 32433972.

DGE-EM 1.00 – Accurate Estimation of Gene Expression Levels from DGE Sequencing Data

DGE-EM 1.00

:: DESCRIPTION

DGE-EM package can be used to infer gene expression levels from 3′-tag Digital Gene Expression (DGE) data. DGE-EM uses a novel expectation-maximization algorithm that takes into account alternative splicing isoforms and tags that map at multiple locations in the genome, and corrects for incomplete digestion and sequencing errors. Experimental results on real DGE data generated from reference RNA samples show that our algorithm outperforms commonly used estimation methods based on unique tag counting as well as estimates obtained from RNA-Seq data for the same samples. Results of a comprehensive simulation study assessing the effect of various experimental parameters suggest that further improvements in estimation accuracy could be achieved by optimizing protocol parameters such as the anchoring enzymes and digestion probability.

::DEVELOPER

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

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Java

:: DOWNLOAD

DGE-EM

:: MORE INFORMATION

Citation:

M. Nicolae and I.I. Mandoiu,
Accurate Estimation of Gene Expression Levels from DGE Sequencing Data,
Invited talk, 1st Annual RECOMB Satellite Workshop on Massively Parallel Sequencing, March 26-27, 2011,