RNAcompete / RNAcompete-S – Analysis of RNA Sequence/structure preferences for RNA binding proteins

RNAcompete / RNAcompete-S

:: DESCRIPTION

RNAcompete is a method for the systematic analysis of RNA binding specificities that uses a single binding reaction to determine the relative preferences of RBPs for short RNAs that contain a complete range of k-mers in structured and unstructured RNA contexts.

RNAcompete-S couples a single-step competitive binding reaction with an excess of random RNA 40-mers to a custom computational pipeline for interrogation of the bound RNA sequences and derivation of SSMs (Sequence and Structure Models).

rnascan is a (mostly) Python suite to scan RNA sequences and secondary structures with sequence and secondary structure PFMs. Secondary structure is represented as weights in different secondary structure contexts, similar to how a PFM represents weights of different nucleotides or amino acids.

::DEVELOPER

Morris Lab , Hughes lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R
  • Python

:: DOWNLOAD

RNAcompete , RNAcompete-S , rnascan

:: MORE INFORMATION

Citation:

Nat Biotechnol. 2009 Jul;27(7):667-70. doi: 10.1038/nbt.1550. Epub 2009 Jun 28.
Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins.
Ray D, Kazan H, Chan ET, Peña Castillo L, Chaudhry S, Talukder S, Blencowe BJ, Morris Q, Hughes TR.

Cook, K.B., Vembu, S., Ha, K.C.H., Zheng, H., Laverty, K.U., Hughes, T.R., Ray, D., Morris, Q.D., 2017.
RNAcompete-S: Combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection.
Methods 126, 18–28.

DAFS 0.0.3 – Simultaneous Aligning and Folding of RNA Sequences by Dual Decomposition

DAFS 0.0.3

:: DESCRIPTION

DAFS is a novel algorithm that simultaneously aligns and folds RNA sequences based on maximizing expected accuracy of a predicted common secondary structure and its alignment.

::DEVELOPER

Computational Biology Research Center (CBRC),

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DAFS

:: MORE INFORMATION

Citation:

DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition.
Sato K, Kato Y, Akutsu T, Asai K, Sakakibara Y.
Bioinformatics. 2012 Dec 15;28(24):3218-24. doi: 10.1093/bioinformatics/bts612.

CentroidFold 0.0.16 – Predict RNA Secondary Structure from RNA Sequence

CentroidFold 0.0.16

:: DESCRIPTION

CentroidFold predicts an RNA secondary structure from an RNA sequence. FASTA and one-sequence-in-a-line format are accepted for predicting a secondary structure per sequence. It also predicts a consensus secondary structure when a multiple alignment (CLUSTALW format) is given. CentroidFold based on a generalized centroid estimator is one of the most accurate tools for predicting RNA secondary structures. See the original paper for the details of the algorithm.

::DEVELOPER

Computational Biology Research Center (CBRC),

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CentroidFold

:: MORE INFORMATION

Citation:

Kengo Sato, Michiaki Hamda, Kiyoshi Asai, Toutai Mituyama,
CentroidFold: a web application for RNA secondary structure prediction,
Nucl. Acids Res. (2009) 37 (suppl 2): W277-W280

BayesPairing – Identifying the presence of 3D Structural Modules from RNA sequences

BayesPairing

:: DESCRIPTION

BayesPairing is an automated, efficient and customizable tool for (i) building Bayesian networks representing RNA 3D modules and (ii) rapid identification of 3D modules in sequences. BayesPairing uses a flexible definition of RNA 3D modules that allows us to consider complex architectures such as multi-branched loops and features multiple algorithmic improvements. BayesPairing can handle a broader range of motifs (versatility) and offers considerable running time improvements (efficiency), opening the door to a broad range of large-scale applications.

::DEVELOPER

Computer Science and Biology at McGill

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • C++ Compiler

:: DOWNLOAD

BayesPairing

:: MORE INFORMATION

Citation:

Nucleic Acids Res. 2019 Apr 23;47(7):3321-3332. doi: 10.1093/nar/gkz102.
Automated, customizable and efficient identification of 3D base pair modules with BayesPairing.
Sarrazin-Gendron R, Reinharz V, Oliver CG, Moitessier N, Waldispühl J.

antaRNA 2.0.1.2 – Ant Colony Optimized RNA Sequence Design

antaRNA 2.0.1.2

:: DESCRIPTION

antaRNA applies the principle of Ant Colony optimization (ACO) to the problem of inverse folding a RNA structure i.e. finding a suitable sequence, which can fold into that structure.

::DEVELOPER

Bioinformatics Group, Albert-Ludwigs-University Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 antaRNA

:: MORE INFORMATION

Citation

antaRNA – Ant Colony Based RNA Sequence Design.
Robert K, Martin M, Rolf B.
Bioinformatics. 2015 May 27. pii: btv319.

PETfold 2.0 / PETcofold 3.2 – Folding of Multiple Alignment of RNA sequences

PETfold 2.0 / PETcofold 3.2

:: DESCRIPTION

PETfold: Phylogenetic, Evolutionary and Thermodynamic folding of a multiple alignment of RNA sequences.

PETcofold: Integrated framework with PETfold to fold and search for RNA-RNA interactions between two multiple alignments of RNA sequences.

::DEVELOPER

Center for non-coding RNA in Technology and Health (RTH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 PETfold , PETcofold

:: MORE INFORMATION

Citation:

Seemann SE, Menzel P, Backofen R, Gorodkin J
The PETfold and PETcofold web servers for intra- and intermolecular structures of multiple RNA sequences.”,
Nucleic Acid Res., 39(Web Server issue):W107-11, 2011.

RNAprofile 2.2 – Secondary structure motif discovery in RNA sequences

RNAprofile 2.2

:: DESCRIPTION

RNAprofile is a software for the discovery of conserved sequence/structural motifs in unaligned RNA sequences

::DEVELOPER

the Pesolelab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows

:: DOWNLOAD

 RNAprofile

:: MORE INFORMATION

Citation:

Giulio Pavesi, Giancarlo Mauri, Marco Stefani and Graziano Pesole
RNAProfile: an algorithm for finding conserved secondary structure motifs in unaligned RNA sequences
Nucl. Acids Res. (2004) 32 (10): 3258-3269.

BBSeq 1.0 – Analysis of RNA Sequence Count Data

BBSeq 1.0

:: DESCRIPTION

BBSeq  (Beta-Binomial modeling of the overdispersion of the RNA-seq count data)is used to identify differential expression in high-throughput count data, such as RNAseq count data which is derived from next-generation sequencing machines. Our modeling design is very flexible. It can not only solve the data with multiple comparisons, but also can find the affect from other covariates, such as age and other counfounding variables.

::DEVELOPER

Yi-Hui Zhou

:: SCREENSHOTS

N/A

::REQUIREMENTS

:: DOWNLOAD

 BBSeq

:: MORE INFORMATION

Citation

Zhou YH, Xia K, Wright FA. (2011)
A Powerful and Flexible Approach to the Analysis of RNA Sequence Count Data.
Bioinformatics (2011) 27 (19): 2672-2678.

SPF 1.1 – Find Structurally Significant Regions in RNA Sequences

SPF 1.1

:: DESCRIPTION

SPF (Structural Pattern Finder) is a tool that identify regions of a given RNA or DNA sequence that possess a high probability of being structured. This is achieved by computing, for each region, the difference between the folding free energy of the native sequence with the average folding free energy of randomized sequences of the same base composition.

::DEVELOPER

Laboratoire de Biologie Informatique et Théorique

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / IRIX / SunOS

:: DOWNLOAD

  SPF

:: MORE INFORMATION

Citation:

I. Barrette, G. Poisson, P. Gendron and F. Major (2000)
Pseudoknots in prion protein mRNAs confirmed by comparative sequence analysis and pattern searching,
Nucleic Acids Res. 2001 February 1; 29(3): 753–758.

ESTScan 3.03 – Detect Coding Regions in DNA/RNA Sequences

ESTScan 3.03

:: DESCRIPTION

ESTScan is a program that can detect coding regions in DNA/RNA sequences, even if they are of low quality (e.g. EST sequences). ESTScan will also detect and correct sequencing errors that lead to frameshifts. ESTScan is not a gene prediction program , nor is it an open reading frame detector. In fact, its strength lies in the fact that it does not require an open reading frame to detect a coding region. As a result, the program may miss a few translated amino acids at either the N or the C terminus, but will detect coding regions with high selectivity and sensitivity.

ESTScan Online Version

::DEVELOPER

The SIB Swiss Institute of Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac OS X

:: DOWNLOAD

 ESTScan

:: MORE INFORMATION

Citation

Lottaz C, Iseli C, Jongeneel CV, Bucher P. (2003)
Modeling sequencing errors by combining Hidden Markov models
Bioinformatics 19, ii103-ii112.

Iseli C, Jongeneel CV, Bucher P. (1999)
ESTScan: a program for detecting, evaluating, and reconstructing potential coding regions in EST sequences.
Proc Int Conf Intell Syst Mol Biol.138-48.