FCP 1.0.7 – Fragment Classification Package

FCP 1.0.7

:: DESCRIPTION

FCP (Fragment classification package) is a homology- and composition-based classifiers for assigning a taxonomic attribution to metagenomic fragments.

::DEVELOPER

Beiko lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX

:: DOWNLOAD

 FCP

:: MORE INFORMATION

Citation

Parks, D.H., MacDonald, N.J., and Beiko, R.G. (2011).
Classifying short genomic fragments from novel lineages using composition and homology.
BMC Bioinformatics, 12:328.

RecoverY – Classification of Y-chromosome Specific Reads

RecoverY

:: DESCRIPTION

RecoverY is a tool for shortlisting enriched reads from a sequencing dataset, based on k-mer abundance. Specifically, it can be used for isolating Y-specific reads from a Y flow-sorted dataset.

::DEVELOPER

Medvedev Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

RecoverY

:: MORE INFORMATION

Citation:

Bioinformatics, 34 (7), 1125-1131 2018 Apr 1
RecoverY : k-mer-based read classification for Y-chromosome-specific sequencing and assembly,
Samarth Rangavittal, Robert S. Harris, Monika Cechova, Marta Tomaszkiewicz, Rayan Chikhi, Kateryna Makova, Paul Medvedev

SKraken – Classification of Short Metagenomic Reads based on filtering uninformative k-mers

SKraken

:: DESCRIPTION

SKraken is an efficient approach to accurately classify metagenomic reads against a set of reference genomes, e.g. the NCBI/RefSeq database.

::DEVELOPER

Matteo Comin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

SKraken

:: MORE INFORMATION

Citation

D. Marchiori, M. Comin
SKraken: Fast and Sensitive Classification of Short Metagenomic Reads based on Filtering Uninformative k-mers“.
In Proceedings of the 10th International Conference on Bioinformatics Models, Methods and Algorithms (Bioinformatics 2017), pp. 59-67

CLIOR v1 – Higher Recall in Metagenomic Sequence Classification Exploiting Overlapping Reads

CLIOR v1

:: DESCRIPTION

CLIOR (CLassification Improvement with Overlapping Reads) is a metagenomic classification method that exploits the information captured by the reads overlap graph of the input dataset in order to improve recall and f-measure.

::DEVELOPER

Matteo Comin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

CLIOR

:: MORE INFORMATION

Citation

BMC Genomics, 18 (Suppl 10), 917 2017 Dec 6
Higher Recall in Metagenomic Sequence Classification Exploiting Overlapping Reads
Samuele Girotto , Matteo Comin , Cinzia Pizzi

ANARCI – Antigen Receptor Numbering And Receptor ClassificatIon

ANARCI

:: DESCRIPTION

ANARCI is a tool for numbering amino-acid sequences of antibody and T-cell receptor variable domains.

::DEVELOPER

Oxford Protein Informatics Group (OPIG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Python

:: DOWNLOAD

 ANARCI

:: MORE INFORMATION

Citation:

ANARCI: Antigen receptor numbering and receptor classification.
Dunbar J, Deane CM.
Bioinformatics. 2015 Sep 30. pii: btv552.

MIDClass 2 – Gene Expression Classification

MIDClass 2

:: DESCRIPTION

MIDClass (Microarray Interval Discriminant CLASSifier) is a new classification method for expression profiling data based on association rules.

::DEVELOPER

MIDClass team

:: SCREENSHOTS

MIDClass

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • JRE

:: DOWNLOAD

 MIDClass

:: MORE INFORMATION

Citation:

MIDClass: microarray data classification by association rules and gene expression intervals.
Giugno R, Pulvirenti A, Cascione L, Pigola G, Ferro A.
PLoS One. 2013 Aug 6;8(8):e69873. doi: 10.1371/journal.pone.0069873. Print 2013.

GLaD – A Mixed-Membership Model for Heterogenous Tumor Subtype Classification

GLaD

:: DESCRIPTION

GLAD is a mixed-membership classification model that simultaneously learns a sparse biomarker signature for each subtype as well as a distribution over subtypes for each sample.

::DEVELOPER

Flaherty Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • Python

:: DOWNLOAD

  GLaD

:: MORE INFORMATION

Citation

GLAD: A mixed-membership model for heterogeneous tumor subtype classification.
Saddiki H, McAuliffe J, Flaherty P.
Bioinformatics. 2014 Sep 29. pii: btu618.

MetaBinG 0.4 / MetaBinG2 – ultra-fast Metagenomic Sequence Classification system using GPUs

MetaBinG 0.4 / MetaBinG2

:: DESCRIPTION

MetaBinG  is an ultra-fast metagenomic sequence classification system using graphic processing units (GPUs).

MetaBinG2 is a fast and accurate metagenomic sequence classification system for samples with many unknown organisms

::DEVELOPER

Dr. Chaochun Wei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MetaBinG / MetaBinG2

:: MORE INFORMATION

Citation:

Biol Direct. 2018 Aug 22;13(1):15. doi: 10.1186/s13062-018-0220-y.
MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms.
Qiao Y, Jia B, Hu Z, Sun C, Xiang Y, Wei C.

PLoS One. 2011;6(11):e25353. doi: 10.1371/journal.pone.0025353. Epub 2011 Nov 23.
MetaBinG: using GPUs to accelerate metagenomic sequence classification.
Jia P1, Xuan L, Liu L, Wei C.

PlantMiRNAPred – Classification of Real and Pseudo Plant Pre-miRNAs

PlantMiRNAPred

:: DESCRIPTION

PlantMiRNAPred web server can be used to classify real plant pre-miRNAs and pseudo hairpins.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 May 15;27(10):1368-76. doi: 10.1093/bioinformatics/btr153. Epub 2011 Mar 26.
PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs.
Xuan P, Guo M, Liu X, Huang Y, Li W, Huang Y.

CancerIN – Classification and Designing of Anticancer Compounds

CancerIN

:: DESCRIPTION

CancerIN  is a web server developed for predicting anticancer activity of molecules. Similarity based approach has been used for discrimination or classification of anticancer and non-anticancer molecule.

::DEVELOPER

CancerIN team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOs
  • Python

:: DOWNLOAD

CancerIN

:: MORE INFORMATION

Citation

Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines.
Singh H, Kumar R, Singh S, Chaudhary K, Gautam A, Raghava GP.
BMC Cancer. 2016 Feb 9;16:77. doi: 10.1186/s12885-016-2082-y.