BANNER 0.2 – Named Entity Recognition System

BANNER 0.2

:: DESCRIPTION

BANNER is a named entity recognition system, primarily intended for biomedical text. It is a machine-learning system based on conditional random fields and contains a wide survey of the best features in recent literature on biomedical named entity recognition (NER). BANNER is portable and is designed to maximize domain independence by not employing semantic features or rule-based processing steps. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text.

::DEVELOPER

BioAI Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 BANNER

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2008:652-63.
BANNER: an executable survey of advances in biomedical named entity recognition.
Leaman R, Gonzalez G.

SEGMER – Protein Substructure Recognition

SEGMER

:: DESCRIPTION

SEGMER is a segmental threading algorithm designed to recoginzing substructure motifs from the Protein Data Bank (PDB) library. It first splits target sequences into segments which consists of 2-4 consecutive or non-consecutive secondary structure elements (alpha-helix, beta-strand). The sequence segments are then threaded through the PDB to identify conserved substructures. It often identifies better conserved structure motifs than the whole-chain threading methods, especially when there is no similar global fold existing in the PDB.

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SEGMER

:: MORE INFORMATION

Citation

Structure. 2010 Jul 14;18(7):858-67. doi: 10.1016/j.str.2010.04.007.
Recognizing protein substructure similarity using segmental threading.
Wu S1, Zhang Y.

SPRING – Accurate Template Recognition for Protein Complex Structure

SPRING

:: DESCRIPTION

SPRING is a template-base algorithm for protein-protein structure prediction. It first threads one chain of the protein complex through the PDB library with the binding parters retrieved from the original oligomer entries. The complex models associated with another chain is deduced from a pre-calculated look-up table, with the best orientation selected by the SPRING-score which is a combination of threading Z-score, interface contacts, and TM-align match between monomer-to-dimer templates.

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

J Chem Inf Model. 2013 Mar 25;53(3):717-25. doi: 10.1021/ci300579r. Epub 2013 Feb 27.
Mapping monomeric threading to protein-protein structure prediction.
Guerler A1, Govindarajoo B, Zhang Y.

SAXSTER – SAXS-assisted Protein Fold Recognition

SAXSTER

:: DESCRIPTION

SAXSTER is a new algorithm to combine small-angle x-ray scattering (SAXS) data and threading for high-resolution protein structure determination. Given a query sequence, SAXSTER first generates a list of template alignments using the MUSTER threading program from the PDB library. The SAXS data will then be used to prioritize the best template alignments based on the SAXS profile match, which are finally used for full-length atomic protein structure construction

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Borwser
:: DOWNLOAD

 NO 

:: MORE INFORMATION

Citation

Biophys J. 2011 Dec 7;101(11):2770-81. doi: 10.1016/j.bpj.2011.10.046.
Improving protein template recognition by using small-angle x-ray scattering profiles.
dos Reis MA1, Aparicio R, Zhang Y.

RW 1.0 – Protein Structure Modeling and Structure Decoy Recognition

RW 1.0

:: DESCRIPTION

RW (Random-Walk) is distance-dependent atomic potential for protein structure modeling and structure decoy recognition. It was derived from 1,383 high-resolution PDB structures using an ideal random-walk chain as the reference state. The RW potential has been extensively optimized and tested on a variety of protein structure decoy sets and demonstrates a significant power in protein structure recognition and a strong correlation with the RMSD of decoys to the native structures

::DEVELOPER

Yang Zhang’s Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

 calRW

:: MORE INFORMATION

Citation

Zhang J, Zhang Y (2010)
A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction.
PLoS ONE 5(10): e15386.

LIBRA v1 / LIBRA+ / LIBRAWA – Ligand Binding site Recognition Application

LIBRA v1 / LIBRA+ / LIBRAWA

:: DESCRIPTION

LIBRA is based on a graph theory approach to find the largest subset of similar residues between an input protein and a collection of known functional sites.

LIBRA+ is an upgraded version of LIBRA, a tool that, given a protein’s structural model, predicts the presence and identity of active sites and/or ligand binding sites. The algorithm implemented by LIBRA+ is based on a graph theory approach to find the largest subset of similar residues between an input protein and a collection of known functional sites. For this purpose, the algorithm makes use of two predefined databases for active sites and ligand binding sites, respectively derived from the Catalytic Site Atlas and the Protein Data Bank.

LIBRA Web Application is an online portal where users can exploit LIBRA+’s capabilities in recognizing the presence and identity of active sites and/or ligand binding sites given a protein’s structural model. With a free registration, users are given a personal space where they can launch and schedule multiple recognitions, check out the resulting three-dimensional alignments and browse ligand clusters. Results produced in LIBRAWA are backward-compatible with LIBRA+ and can thus be exported in LIBRA+’s format to be accessed offline from the desktop application.

::DEVELOPER

the Theoretical Biology and Bioinformatics Laboratory

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/Linux
  • JRE

:: DOWNLOAD

 LIBRA / LIBRA+

:: MORE INFORMATION

Citation

Toti D, Viet Hung L, Tortosa V, Brandi V, Polticelli F.
LIBRA-WA: a web application for ligand binding site detection and protein function recognition.
Bioinformatics. 2018 Mar 1;34(5):878-880. doi: 10.1093/bioinformatics/btx715. PMID: 29126218; PMCID: PMC6192203.

LIBRA: LIgand Binding site Recognition Application.
Viet Hung L, Caprari S, Bizai M, Toti D, Polticelli F.
Bioinformatics. 2015 Aug 26. pii: btv489.

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

 NO

:: 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.