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Integrative approach for detecting membrane proteins.

Author(s): Alballa M, Butler G

BACKGROUND: Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane protein...

Article GUID: 33349234
BENIN: Biologically enhanced network inference.

Author(s): Wonkap SK, Butler G

J Bioinform Comput Biol. 2020 Jun;18(3):2040007 Authors: Wonkap SK, Butler G

Article GUID: 32698722
TooT-T: discrimination of transport proteins from non-transport proteins.

Author(s): Alballa M, Butler G

BMC Bioinformatics. 2020 Apr 23;21(Suppl 3):25 Authors: Alballa M, Butler G

Article GUID: 32321420
TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information.

Author(s): Alballa M, Aplop F, Butler G

PLoS One. 2020;15(1):e0227683 Authors: Alballa M, Aplop F, Butler G

Article GUID: 31935244
Analytical and computational approaches to define the Aspergillus niger secretome.

Author(s): Tsang A, Butler G, Powlowski J, Panisko EA, Baker SE

Fungal Genet Biol. 2009 Mar;46 Suppl 1:S153-S160 Authors: Tsang A, Butler G, Powlowski J, Panisko EA, Baker SE

Article GUID: 19618504
SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models.

Author(s): Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

BMC Bioinformatics. 2014 Jul 01;15:229 Authors: Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

Article GUID: 24980894
Machine learning for biomedical literature triage.

Author(s): Almeida H, Meurs MJ, Kosseim L, Butler G, Tsang A

PLoS One. 2014;9(12):e115892 Authors: Almeida H, Meurs MJ, Kosseim L, Butler G, Tsang A

Article GUID: 25551575
mycoCLAP, the database for characterized lignocellulose-active proteins of fungal origin: resource and text mining curation support.

Author(s): Strasser K, McDonnell E, Nyaga C, Wu M, Wu S, Almeida H, Meurs MJ, Kosseim L, Powlowski J, Butler G, Tsang A

Database (Oxford). 2015;2015: Authors: Strasser K, McDonnell E, Nyaga C, Wu M, Wu S, Almeida H, Meurs MJ, Kosseim L, Powlowski J, Butler G, Tsang A

Article GUID: 25754864
An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures.

Author(s): Zandi K, Butler G, Kharma N

Front Genet. 2016;7:129 Authors: Zandi K, Butler G, Kharma N

Article GUID: 27499762
Title:An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures.
Authors:Zandi K,  Butler G,  Kharma N
Link:https://www.ncbi.nlm.nih.gov/pubmed/27499762?dopt=Abstract
DOI:10.3389/fgene.2016.00129
Category:Front Genet
PMID:27499762
Dept Affiliation: GENOMICS
1 Computer Science Department, Concordia University Montreal, QC, Canada.
2 Computer Science Department, Concordia UniversityMontreal, QC, Canada; Centre for Structural and Functional Genomics, Concordia UniversityMontreal, QC, Canada.
3 Centre for Structural and Functional Genomics, Concordia UniversityMontreal, QC, Canada; Electrical and Computer Engineering Department, Concordia UniversityMontreal, QC, Canada.

Description:

An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures.

Front Genet. 2016;7:129

Authors: Zandi K, Butler G, Kharma N

Abstract

Computational design of RNA sequences that fold into targeted secondary structures has many applications in biomedicine, nanotechnology and synthetic biology. An RNA molecule is made of different types of secondary structure elements and an important RNA element named pseudoknot plays a key role in stabilizing the functional form of the molecule. However, due to the computational complexities associated with characterizing pseudoknotted RNA structures, most of the existing RNA sequence designer algorithms generally ignore this important structural element and therefore limit their applications. In this paper we present a new algorithm to design RNA sequences for pseudoknotted secondary structures. We use NUPACK as the folding algorithm to compute the equilibrium characteristics of the pseudoknotted RNAs, and describe a new adaptive defect weighted sampling algorithm named Enzymer to design low ensemble defect RNA sequences for targeted secondary structures including pseudoknots. We used a biological data set of 201 pseudoknotted structures from the Pseudobase library to benchmark the performance of our algorithm. We compared the quality characteristics of the RNA sequences we designed by Enzymer with the results obtained from the state of the art MODENA and antaRNA. Our results show our method succeeds more frequently than MODENA and antaRNA do, and generates sequences that have lower ensemble defect, lower probability defect and higher thermostability. Finally by using Enzymer and by constraining the design to a naturally occurring and highly conserved Hammerhead motif, we designed 8 sequences for a pseudoknotted cis-acting Hammerhead ribozyme. Enzymer is available for download at https://bitbucket.org/casraz/enzymer.

PMID: 27499762 [PubMed]