Kinannote 1.0 – Protein Kinase Identification and Classification

Kinannote 1.0

:: DESCRIPTION

Kinannote identifies and classifies protein kinases in a user-provided fasta file using an HMM derived from serine/threonine protein kinases, a position specific scoring matrix derived from the HMM, and comparison with a local version of the curated kinase database from kinase.com. If the user inputs a complete proteome, additional modules evaluate the completeness of the kinome and place it in context with reference kinomes.

::DEVELOPER

Kinannote team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • hmmer 2
  • Blast 2.24

:: DOWNLOAD

  Kinannote

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Oct 1;29(19):2387-94. doi: 10.1093/bioinformatics/btt419. Epub 2013 Jul 31.
Kinannote, a computer program to identify and classify members of the eukaryotic protein kinase superfamily.
Goldberg JM, Griggs AD, Smith JL, Haas BJ, Wortman JR, Zeng Q.

GeneSelector 1.0 – Find Small subset of Genes for Classification of Expression data

GeneSelector 1.0

:: DESCRIPTION

GeneSelector finds a small subset of genes for classification of expression data.

::DEVELOPER

Ari Frank. @Laboratory of Computational Biology , Technion

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 GeneSelector

:: MORE INFORMATION

StoatyDive v1.1.0 – Evaluation and Classification of Peak Profiles for Sequencing data

StoatyDive v1.1.0

:: DESCRIPTION

StoatyDive is a tool to evaluate and classify predicted peak profiles to assess the binding specificity of a protein to its targets. It can be used for sequencing data such as CLIP-seq or ChIP-Seq, or any other type of peak profile data.

::DEVELOPER

Bioinformatics Group Albert-Ludwigs-University Freiburg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

StoatyDive

:: MORE INFORMATION

Citation

Heyl F, Backofen R.
StoatyDive: Evaluation and classification of peak profiles for sequencing data.
Gigascience. 2021 Jun 18;10(6):giab045. doi: 10.1093/gigascience/giab045. PMID: 34143874; PMCID: PMC8212874.

AUDACITY 0.2 – AUtozygosity iDentification And ClassIfication Tool

AUDACITY 0.2

:: DESCRIPTION

AUDACITY is novel computational approach for the identification of Runs of Homozygosity by using VCF files from whole-exome and whole-genome sequencing data generated by second generation sequencing technologies.

::DEVELOPER

AUDACITY team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • MacOsX/Linux

:: DOWNLOAD

AUDACITY 

:: MORE INFORMATION

Citation

Magi A, Giangregorio T, Semeraro R, Carangelo G, Palombo F, Romeo G, Seri M, Pippucci T.
AUDACITY: A comprehensive approach for the detection and classification of Runs of Homozygosity in medical and population genomics.
Comput Struct Biotechnol J. 2020 Jul 14;18:1956-1967. doi: 10.1016/j.csbj.2020.07.003. PMID: 32774790; PMCID: PMC7394861.

ChroMoS – SNP Classification, Prioritization and Functional Interpretation

ChroMoS

:: DESCRIPTION

ChroMoS (Chromatin Modified SNPs) combines genetic and epigenetic data to facilitate SNP classification, prioritization and prediction of their functional effect.

::DEVELOPER

Bioinformatics and Next Generation Sequencing Group; Max Planck Institute of Immunobiology and Epigenetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

ChroMoS: an integrated web tool for SNP classification, prioritization and functional interpretation.
Barenboim M, Manke T.
Bioinformatics. 2013 Sep 1;29(17):2197-8. doi: 10.1093/bioinformatics/btt356.

WGSQuikr 1.0.0 – Whole-genome Shotgun Metagenomic Classification

WGSQuikr 1.0.0

:: DESCRIPTION

WGSQuikr is a very rapid, whole-genome shotgun metagenomic reconstruction.

::DEVELOPER

WGSQuikr team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX /Windows
  • MatLab

:: DOWNLOAD

 WGSQuikr

:: MORE INFORMATION

Citation

PLoS One. 2014 Mar 13;9(3):e91784. doi: 10.1371/journal.pone.0091784. eCollection 2014.
WGSQuikr: fast whole-genome shotgun metagenomic classification.
Koslicki D, Foucart S, Rosen G.

Q5 – Classification of Complete Mass Spectra of a Complex Protein Mixture

Q5

:: DESCRIPTION

Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is non-iterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques, and can provide clues as to the molecular identities of differentially-expressed proteins and peptides.

::DEVELOPER

Donald Lab at Duke University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • Matlab

:: DOWNLOAD

Q5

:: MORE INFORMATION

Citation

Probabilistic Disease Classification of Expression-Dependent Proteomic Data from Mass Spectrometry of Human Serum
Ryan H. Lilien, Hany Farid and Bruce R. Donald
Journal of Computational Biology, 2003; 10(6): 925-946.

ganon 1.0.0 – Read Classification tool based on Interleaved Bloom Filters

ganon 1.0.0

:: DESCRIPTION

ganon is a k-mer based DNA classification tool using Interleaved Bloom Filters for short reads.

::DEVELOPER

Vitor C. Piro

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

ganon

:: MORE INFORMATION

Citation

Piro VC, Dadi TH, Seiler E, Reinert K, Renard BY.
ganon: precise metagenomics classification against large and up-to-date sets of reference sequences.
Bioinformatics. 2020 Jul 1;36(Suppl_1):i12-i20. doi: 10.1093/bioinformatics/btaa458. PMID: 32657362; PMCID: PMC7355301.

pathClass 0.9.4 – Classification using Biological Pathways as prior knowledge

pathClass 0.9.4

:: DESCRIPTION

pathClass is a collection of classification methods that use information about feature connectivity in a biological network as an additional source of information. This additional knowledge is incorporated into the classification a priori. Several authors have shown that this approach significantly increases the classification performance.

::DEVELOPER

pathClass team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

 pathClass

:: MORE INFORMATION

Citation

Bioinformatics. 2011 May 15;27(10):1442-3. doi: 10.1093/bioinformatics/btr157. Epub 2011 Mar 30.
pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery.
Johannes M1, Fröhlich H, Sültmann H, Beissbarth T.

EFFECT 2013 – Automated Construction and Extraction of Features for Classification of Biological Sequences

EFFECT 2013

:: DESCRIPTION

EFFECT is an algorithmic framework for automated detection of functional signals in biological sequences.

::DEVELOPER

Computational Biology lab, George Mason University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java
  • BioJava

:: DOWNLOAD

 EFFECT

:: MORE INFORMATION

Citation

Kamath U, De Jong K, Shehu A.
Effective automated feature construction and selection for classification of biological sequences.
PLoS One. 2014 Jul 17;9(7):e99982. doi: 10.1371/journal.pone.0099982. PMID: 25033270; PMCID: PMC4102475.