Tag: Signal

PathoGiST v0.3.6 – Clustering Pathogen Isolates by combining multiple Genotyping Signals

PathoGiST v0.3.6

:: DESCRIPTION

PathOGiST is an algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • conda

:: DOWNLOAD

PathOGiST

:: MORE INFORMATION

Citation

Katebi M. et al. (2020)
PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals.
Algorithms for Computational Biology. AlCoB 2020. Lecture Notes in Computer Science, vol 12099. Springer, Cham. https://doi.org/10.1007/978-3-030-42266-0_9

polyadq – Detection of Human Polyadenylation Signals

polyadq

:: DESCRIPTION

polyadq is a program for detection of human polyadenylation signals. To avoid training on possibly flawed data, the development of polyadq began with a de novo characterization of human mRNA 3′ processing signals. This information was used in training two quadratic discriminant functions that polyadq uses to evaluate potential polyA signals.

::DEVELOPER

Michael Zhang Computational Biology Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Gene. 1999 Apr 29;231(1-2):77-86.
Detection of polyadenylation signals in human DNA sequences.
Tabaska JE, Zhang MQ.

SeqNLS – Nuclear Localization Signal Prediction

SeqNLS

:: DESCRIPTION

SeqNLS is a sequential pattern mining algorithmto effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs.

::DEVELOPER

Machine Learning and Evolution Laboratory (MLEG)

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web Browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

PLoS One. 2013 Oct 29;8(10):e76864. doi: 10.1371/journal.pone.0076864. eCollection 2013.
SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring.
Lin JR1, Hu J.

ODIN 0.4.1 – Detecting Differential Peaks in ChIP-seq Signals

ODIN 0.4.1

:: DESCRIPTION

ODIN is a HMM-based approach to detect and analyse differential peaks in pairs of ChIP-seq data. It is the first differential peak caller that performs genomic signal processing, peak calling and p-value calculation in an integrated framework.

::DEVELOPER

IZKF Computational Biology and Bioinformatics Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 ODIN

:: MORE INFORMATION

Citation:

Detecting differential peaks in ChIP-seq signals with ODIN.
Allhoff M, Seré K, Chauvistré H, Lin Q, Zenke M, Costa IG.
Bioinformatics. 2014 Nov 3. pii: btu722.

TPpred 3.0 – Detection of Mitochondrial-targeting Signals in Proteins

TPpred 3.0

:: DESCRIPTION

TPpred is a web server for MITOCHONDRIAL targeting peptides prediction in proteins. TPpred is optimized for the prediction of cleavage sites of mitochondrial targeting peptides.

::DEVELOPER

Bologna Biocomputing Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • BioPython
  • EMBOSS

:: DOWNLOAD

  TPpred

:: MORE INFORMATION

Citation

TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in Eukaryotic proteins.
Savojardo C, Martelli PL, Fariselli P, Casadio R.
Bioinformatics. 2015 Jun 16. pii: btv367

TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs.
Savojardo C, Martelli PL, Fariselli P, Casadio R.
Bioinformatics. 2014 Jun 27. pii: btu411.

BIDCHIPS – Bias-Decomposition of ChIP-seq Signals

BIDCHIPS

:: DESCRIPTION

BIDCHIPS is a software for quantifying and Removing Biases in ChIP-Seq Signals

::DEVELOPER

Perkins Lab at the OHRI

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux / MacOsX /Windows
  •  MatLab / R

:: DOWNLOAD

  BIDCHIPS

:: MORE INFORMATION

Citation

BIDCHIPS: bias decomposition and removal from ChIP-seq data clarifies true binding signal and its functional correlates.
Ramachandran P, Palidwor GA, Perkins TJ.
Epigenetics Chromatin. 2015 Sep 17;8:33. doi: 10.1186/s13072-015-0028-2.

SignalSpider – Probabilistic Modeling and Pattern Discovery on Multiple Normalized ChIP-Seq Signal Profile

SignalSpider

:: DESCRIPTION

SignalSpider is a probabilistic model for deciphering the combinatorial binding of DNA-binding proteins. The model (SignalSpider) aims at modeling and extracting patterns from multiple ChIP-Seq profiles.

::DEVELOPER

Ka-Chun Wong

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • MCR (Matlab Compiler Runtime)

:: DOWNLOAD

 SignalSpider

:: MORE INFORMATION

Citation

SignalSpider: Probabilistic Pattern Discovery on Multiple Normalized ChIP-Seq Signal Profiles.
Wong KC, Li Y, Peng C, Zhang Z.
Bioinformatics. 2014 Sep 5. pii: btu604.

NLStradamus 1.8 – Hidden Markov Model for Nuclear Localization Signal Prediction

NLStradamus 1.8

:: DESCRIPTION

NLStradamus predicts NLSs in nuclear proteins that are transported by the import machinery of the cell.

::DEVELOPER

Alan Moses’ Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NLStradamus

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2009 Jun 29;10:202. doi: 10.1186/1471-2105-10-202.
NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction.
Nguyen Ba AN1, Pogoutse A, Provart N, Moses AM.

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