GENSCAN 20030218 – Identification of complete Gene Structures in Genomic DNA

GENSCAN 20030218

:: DESCRIPTION

GENSCAN identifies complete exon/intron structures of genes in genomic DNA. Novel features of the program include the capacity to predict multiple genes in a sequence, to deal with partial as well as complete genes, and to predict consistent sets of genes occurring on either or both DNA strands. GENSCAN is shown to have substantially higher accuracy than existing methods when tested on standardized sets of human and vertebrate genes, with 75 to 80% of exons identified exactly. The program is also capable of indicating fairly accurately the reliability of each predicted exon

GENSCAN Online Version

::DEVELOPER

Christopher Burge Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 GENSCAN

:: MORE INFORMATION

Citation

Prediction of complete gene structures in human genomic DNA.
Burge C, Karlin S.
J Mol Biol. 1997 Apr 25;268(1):78-94.

HBPred 2.0 – Identification of Hormone-binding Protein

HBPred 2.0

:: DESCRIPTION

HBPred is a webserver for the identification of hormone-binding protein (HBP). In the predictor, protein sequences were coded by tripeptide composition method and binomial distribution method.

::DEVELOPER

LinDing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Identification of hormone binding proteins based on machine learning methods.
Tan JX, Li SH, Zhang ZM, Chen CX, Chen W, Tang H, Lin H.
Math Biosci Eng. 2019 Mar 22;16(4):2466-2480. doi: 10.3934/mbe.2019123.

iNuc-PhysChem – Identification of Nucleosomes in S.cerevisiae Genome

iNuc-PhysChem

:: DESCRIPTION

iNuc-PhysChem is a sequence-based predictor for identifying nucleosomes via physicochemical properties

::DEVELOPER

LinDing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PLoS One. 2012;7(10):e47843. doi: 10.1371/journal.pone.0047843. Epub 2012 Oct 29.
iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties.
Chen W1, Lin H, Feng PM, Ding C, Zuo YC, Chou KC.

ARIBA 2.14.4 – Antimicrobial Resistance Identification By Assembly

ARIBA 2.14.4

:: DESCRIPTION

ARIBA is a tool that identifies antibiotic resistance genes by running local assemblies.

::DEVELOPER

Pathogen Informatics, Wellcome Trust Sanger Institute

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

ARIBA

:: MORE INFORMATION

Citation

J Glob Antimicrob Resist. 2019 Sep 7;19:129-131. doi: 10.1016/j.jgar.2019.09.001. [Epub ahead of print]
Draft genome of a macrolide resistant XDR Salmonella enterica serovar Paratyphi A strain using a shotgun sequencing approach.
Khatoon A, Malik HMT, Aurongzeb M, Raza SA, Karim A.

AbundantOTU+ 0.93b – Identification and Quantification of Abundant Species from Pyrosequences of 16S rRNA

AbundantOTU+ 0.93b

:: DESCRIPTION

AbundantOTU+ deals with sequences from rare species as well, compared to AbundantOTU!!

AbundantOTU is a software based on a Consensus Alignment (CA) algorithm,which infers consensus sequences, each representing an OTU,taking advantage of the sequence redundancy for abundant species. Pyrosequencing reads can then be recruited to the consensus sequences to give quantitative information for the corresponding species. As tested on 16S rRNA pyrosequence datasets from mock communities with known species, AbundantOTU rapidly reported identiffied sequences of the source 16S rRNAs and the abundances of the corresponding species.AbundantOTU was also applied to 16S rRNA pyrosequence datasets derived from real microbial communities and the results are in general agreement with previous studies.

::DEVELOPER

Yuzhen Ye lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 AbundantOTU+

:: MORE INFORMATION

Citation:

Yuzhen Ye.
Identification and quantification of abundant species from pyrosequences of 16S rRNA by consensus alignment.
The Proceedings of BIBM 2010, 153-157

LncADeep – ab initio lncRNA Identification and Functional Annotation tool

LncADeep

:: DESCRIPTION

LncADeep is an ab initio lncRNA identification and functional annotation tool based on deep learning.First, LncADeep identifies lncRNAs by integrating sequence intrinsic and homology features based on deep belief networks. Second, LncADeep predicts lncRNA-protein interactions using sequence and structure features based on deep neural networks. Third, since accurate lncRNA-protein interactions can help to infer the functions of lncRNAs.

::DEVELOPER

ZhuLab, Peking Uiniversity, Beijing

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WebBrowser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Bioinformatics. 2018 Nov 15;34(22):3825-3834. doi: 10.1093/bioinformatics/bty428.
LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning.
Yang C, Yang L, Zhou M, Xie H, Zhang C, Wang MD, Zhu H.

iKNN – Classifier for Non-coding RNA Identification in Organelle Genome level

iKNN

:: DESCRIPTION

According to the concept of the K-nearest neighbor technique, a novel decision-making method, an improved K-nearest neighbor classifier (iKNN) which not only use the information of quantity but also similarity distance, is introduced to recognize ncRNAs from different organelle genomes.

::DEVELOPER

The Li’s Group of Theoretical Biophysics and Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

  iKNN

:: MORE INFORMATION

Citation

Using the Chou’s pseudo component to predict the ncRNA locations based on the improved K-nearest neighbor (iKNN) classifier.
In prepared.

SimTandem 1.1.96 – Protein Sequence Identification

SimTandem 1.1.96

:: DESCRIPTION

SimTandem is a freely available tool for identification of peptides from LC-MS/MS spectra. It is based on a similarity search of mass spectra in a database of theoretical spectra generated from a database of known protein sequences.

::DEVELOPER

SIRET Research Group

:: SCREENSHOTS

SimTandem

:: REQUIREMENTS

  • Windows/Linux
  • OpenMS

:: DOWNLOAD

 SimTandem

:: MORE INFORMATION

Citation:

Jiri Novak, Timo Sachsenberg, David Hoksza, Tomas Skopal and Oliver Kohlbacher.
On Comparison of SimTandem with State-of-the-Art Peptide Identification Tools, Efficiency of Precursor Mass Filter and Dealing with Variable Modifications.
Journal of Integrative Bioinformatics, 10(3):228, 2013.

SeqSite 1.0.0 – ChIP-Seq Binding Site Identification

SeqSite 1.0.0

:: DESCRIPTION

SeqSite was developed for detecting transcription factor binding sites from ChIP-seq data.SeqSite is an efficient and easy-to-use software tool implementing a novel method for identifying and pinpointing transcription factor binding sites. It first detects transcription factor binding regions by clustering tags and statistical hypothesis testing, and locates every binding site in detected binding regions by modeling the tag profiles. It can pinpoint closely spaced adjacent binding sites from ChIP-seq data.

::DEVELOPER

SeqSite team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • C Compiler

:: DOWNLOAD

  SeqSite

:: MORE INFORMATION

Citation

Xi Wang and Xuegong Zhang.
Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite.
BMC Systems Biology, 5(Suppl 2):S3.

ClustEx 2.0 – Responsive Gene Module Identification package

ClustEx 2.0

:: DESCRIPTION

ClustEx, a two-step method based on the new formulation, was developed and applied to identify the responsive gene modules of human umbilical vein endothelial cells (HUVECs) in inflammation and angiogenesis models by integrating the time-course microarray data and genome-wide PPI data. It shows better performance than several available module identification tools by testing on the reference responsive gene sets. Gene set analysis of KEGG pathways, GO terms and microRNAs (miRNAs) target gene sets further supports the ClustEx predictions.

::DEVELOPER

Bioinformatics & Intelligent Information Processing Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows

:: DOWNLOAD

  ClustEx

:: MORE INFORMATION

Citation

Jin Gu, Yang Chen, Shao Li, Yanda Li.
Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis.
BMC Systems Biology 2010, 4:47.