TIPR – Transcription Initiation Pattern Recognition on a Genome Scale

TIPR

:: DESCRIPTION

TIPR (Transcription Initiation Pattern Recognizer) is a sequence-based machine learning model that identifies TSSs with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize.

::DEVELOPER

Megraw Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

TIPR

:: MORE INFORMATION

Citation

TIPR: transcription initiation pattern recognition on a genome scale.
Morton T, Wong WK, Megraw M.
Bioinformatics. 2015 Aug 8. pii: btv464.

LDExplorer 1.0.3 – Whole-genome LD-based Haplotype Block Recognition

LDExplorer 1.0.3

:: DESCRIPTION

LDExplorer is an R package for the memory efficient whole-genome LD-based haplotype block recognition.

::DEVELOPER

the Center of Biomedicine (CBM) at EURAC research.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/windows/MacOsX
R package

:: DOWNLOAD

LDExplorer

:: MORE INFORMATION

pGenTHREADER 8.9 – Protein Fold Recognition by Profile-profile Threading

pGenTHREADER 8.9

:: DESCRIPTION

pGenTHREADER and pDomTHREADER is two improved versions of the GenTHREADER protocol for recognizing and aligning protein sequences and demonstrate their application to structure prediction and superfamily discrimination. The two versions use the same core alignment algorithm and in both cases accept features derived from common inputs: protein sequence profiles and structural information. However, the representation and combinations of these features differ between the methods and scoring and confidence values have been tuned to optimize performance in each application domain.

:DEVELOPER

Bioinformatics Group – University College London

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

pGenTHREADER

:: MORE INFORMATION

Citation:

Lobley, A., Sadowski, M.I. & Jones, D.T. (2009)
pGenTHREADER and pDomTHREADER:New Methods For Improved Protein Fold Recognition and Superfamily Discrimination.
Bioinformatics. 25, 1761-1767.

CROSS / CROSSalign / CROSSalive – Recognition of RNA Secondary Structure

CROSS / CROSSalign / CROSSalive

:: DESCRIPTION

CROSS predicts the secondary structure propensity profile of an RNA molecule at single-nucleotide resolution. CROSS produces a table with the propensity scores and a graphical representation of the profile.

CROSSalign computes the similarity of RNA secondary structure

CROSSalive computes the structure of RNA molecules in vivo. Changes of structure upon N6-Methyladenosine methylation can be predicted.

::DEVELOPER

Tartaglia Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

CROSSalive: a web server for predicting the in vivo structure of RNA molecules.
Delli Ponti R, Armaos A, Vandelli A, Tartaglia GG.
Bioinformatics. 2019 Aug 28. pii: btz666. doi: 10.1093/bioinformatics/btz666.

Front Mol Biosci. 2018 Dec 3;5:111. doi: 10.3389/fmolb.2018.00111. eCollection 2018.
A Method for RNA Structure Prediction Shows Evidence for Structure in lncRNAs.
Delli Ponti R, Armaos A, Marti S, Tartaglia GG

A high-throughput approach to profile RNA structure.
Delli Ponti R, Marti S, Armaos A, Tartaglia GG.
Nucleic Acids Res. 2017 Mar 17;45(5):e35. doi: 10.1093/nar/gkw1094.

CapR 1.1.1 – Revealing Structural Specificities of RNA-binding protein Target Recognition

CapR 1.1.1

:: DESCRIPTION

CapR calculates probabilities that each RNA base position is located within each secondary structural context for long RNA sequences.

::DEVELOPER

Tsukasa Fukunaga

::REQUIREMENTS

Linux
Vienna RNA package

:: DOWNLOAD

CapR

:: MORE INFORMATION

Citation

Genome Biol. 2014 Jan 21;15(1):R16. doi: 10.1186/gb-2014-15-1-r16.
CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data.
Fukunaga T, Ozaki H, Terai G, Asai K, Iwasaki W, Kiryu H.

Phyre 2.0 – Protein Homology/analogY Recognition Engine

Phyre 2.0

:: DESCRIPTION

Phyre2 is a major update to the original Phyre server. As with Phyre, the new system is designed around the idea that you have a protein sequence/gene and want to predict its three-dimensional (3D) structure. Whereas Phyre used a profile-profile alignment algorithm, Phyre2 uses the alignment of hidden Markov models via HHsearch1 to significantly improve accuracy of alignment and detection rate.

::DEVELOPER

Structural Bioinformatics Group, Imperial College

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

The Phyre2 web portal for protein modeling, prediction and analysis.
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ.
Nat Protoc. 2015 Jun;10(6):845-58. doi: 10.1038/nprot.2015.053.

Protein structure prediction on the Web: a case study using the Phyre server.
Kelley LA, Sternberg MJ.
Nat Protoc. 2009;4(3):363-71. doi: 10.1038/nprot.2009.2.

WDRR – WD40 Repeat Recognition

WDRR

:: DESCRIPTION

WDRR is a new WD40 Repeat Recognition method, which uses predicted secondary structure information to generate candidate repeat segments, and further employs a profile-profile alignment to identify the correct WD40 repeats from candidate segments.

::DEVELOPER

Ziding Zhang’s Lab, China Agricultural University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Server
PHP

:: DOWNLOAD

WDRR

:: MORE INFORMATION

Citation

J Theor Biol. 2016 Jun 7;398:122-9. doi: 10.1016/j.jtbi.2016.03.025.
Identification of WD40 repeats by secondary structure-aided profile-profile alignment.
Wang C, Dong X, Han L, Su XD, Zhang Z, Li J, Song J

TIM-Finder – A TIM-barrel Fold Recognition System

TIM-Finder

:: DESCRIPTION

TIM-Finder (Tim-barrel Fold Recognition System) is a computational tool to predict if a query sequence belongs to TIM-barrel protein or not.

::DEVELOPER

Ziding Zhang’s Lab, China Agricultural University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Server

:: DOWNLOAD

TIM-Finder

:: MORE INFORMATION

Citation

BMC Struct Biol. 2009 Dec 14;9:73. doi: 10.1186/1472-6807-9-73.
TIM-Finder: a new method for identifying TIM-barrel proteins.
Si JN1, Yan RX, Wang C, Zhang Z, Su XD.

LINNAEUS 2.0 – Species Name Recognition and Normalization software

LINNAEUS 2.0

:: DESCRIPTION

LINNAEUS is a general-purpose dictionary matching software, capable of processing multiple types of document formats in the biomedical domain (MEDLINE, PMC, BMC, OTMI, text, etc.). It can produce multiple types of output (XML, HTML, tab-separated-value file, or save to a database). It also contains methods for acting as a server (including load balancing across several servers), allowing clients to request matching over a network. A package with files for recognizing and identifying species names is available for LINNAEUS, showing 94% recall and 97% precision compared to LINNAEUS-species-corpus.

::DEVELOPER

the Bergman lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
Java

:: DOWNLOAD

LINNAEUS

:: MORE INFORMATION

Citation

Martin Gerner, Goran Nenadic and Casey M Bergman
LINNAEUS: A species name identification system for biomedical literature
BMC Bioinformatics 2010, 11:85

GNAT 1.22 – Gene / Protein Named Entity Recognition and Normalization software

GNAT 1.22

:: DESCRIPTION

GNAT (Gene Name Normalization) is a library and web service capable of performing gene entity NER and normalization of biomedical articles. Mentions of genes and proteins in the articles are linked to to Entrez Gene identifiers.

::DEVELOPER

the Bergman lab.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
Java

:: DOWNLOAD

GNAT

:: MORE INFORMATION

Citation

Hakenberg et al. (2011)
The GNAT library for local and remote gene mention normalization
Bioinformatics (2011) 27 (19): 2769-2771.