Tag: Identification

PseudoDomain 20130916 – Pseudogene Identification tool

PseudoDomain 20130916

:: DESCRIPTION

PseudoDomain is used to identify processed pseudogenes of mammalian genomes. Unlike most existing tools, it is based on profile HMM-based homology search. It searches genomic sequences against protein domain database.Therefore, when protein annotations of the genome is not available, it still can accurately identify processed pseudogenes.

::DEVELOPER

Yuan Zhang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • G++

:: DOWNLOAD

 PseudoDomain

:: MORE INFORMATION

Citation

Y. Zhang and Y. Sun,
PseudoDomain: identification of processed pseudogenes based on protein domain classification,
ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB), 2012. Pages 178-185

ShatterSeek v0.5 – Identification of Chromothripsis Events from Whole-genome Sequencing data

ShatterSeek v0.5

:: DESCRIPTION

ShatterSeek is an R package that provides utilities to detect chromothripsis events from next-generation sequencing (NGS) data. It takes as input copy number (CN) and structural variation (SV) calls calculated with the user preferred method. Hence, it is compatible with virtually any CN and SV caller.

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • R

:: DOWNLOAD

ShatterSeek

:: MORE INFORMATION

Citation

Cortés-Ciriano I, Lee JJ, Xi R, Jain D, Jung YL, Yang L, Gordenin D, Klimczak LJ, Zhang CZ, Pellman DS; PCAWG Structural Variation Working Group, Park PJ; PCAWG Consortium.
Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing.
Nat Genet. 2020 Mar;52(3):331-341. doi: 10.1038/s41588-019-0576-7. Epub 2020 Feb 5. Erratum in: Nat Genet. 2020 May 13;: PMID: 32025003; PMCID: PMC7058534.

MosaicForecast – Identification of Somatic Mutation from bulk Whole-genome Sequencing data

MosaicForecast

:: DESCRIPTION

MosaicForecast is machine learning method that leverages read-based phasing and read-level features to accurately detect mosaic SNVs (SNPs, small indels) from NGS data. It builds on existing algorithms to achieve a multifold increase in specificity.

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R
  • Python

:: DOWNLOAD

MosaicForecast

:: MORE INFORMATION

Citation

Dou Y, Kwon M, Rodin RE, Cortés-Ciriano I, Doan R, Luquette LJ, Galor A, Bohrson C, Walsh CA, Park PJ.
Accurate detection of mosaic variants in sequencing data without matched controls.
Nat Biotechnol. 2020 Mar;38(3):314-319. doi: 10.1038/s41587-019-0368-8. Epub 2020 Jan 6. PMID: 31907404; PMCID: PMC7065972.

IMMP – Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNA

IMMP

:: DESCRIPTION

IMMP explores two approaches for the investigation of gene-miRNA relationships by integrating mRNA expression and protein expression data. It presents ranked miRNAs lists according to their effects on cancer development and constructed modules containing mRNAs, proteins, and miRNAs, in which these three molecular types are highly correlated.

::DEVELOPER

Data Mining & Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

IMMP

:: MORE INFORMATION

Citation

Seo J, Jin D, Choi CH, Lee H.
Integration of MicroRNA, mRNA, and Protein Expression Data for the Identification of Cancer-Related MicroRNAs.
PLoS One. 2017 Jan 5;12(1):e0168412. doi: 10.1371/journal.pone.0168412. PMID: 28056026; PMCID: PMC5215789.

VToD – Voting based Cancer module Identification by combining Topological and Data-driven Properties

VToD

:: DESCRIPTION

VToD is a voting-based cancer module identification algorithm by combining topological and data-driven properties by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information.

::DEVELOPER

Data Mining & Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 VToD

:: MORE INFORMATION

Citation

PLoS One. 2013 Aug 5;8(8):e70498. doi: 10.1371/journal.pone.0070498. Print 2013.
Voting-based cancer module identification by combining topological and data-driven properties.
Azad AK1, Lee H.

DiME – Disease Module Identification

DiME

:: DESCRIPTION

DiME is a novel algorithm to identify putative disease modules from biological networks.

::DEVELOPER

Shan He Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R package

:: DOWNLOAD

 DiME

:: MORE INFORMATION

Citation

DiME: a scalable disease module identification algorithm with application to glioma progression.
Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S.
PLoS One. 2014 Feb 11;9(2):e86693. doi: 10.1371/journal.pone.0086693.

GenAPI v1.0 – Gene Absence Presence Identification tool

GenAPI v1.0

:: DESCRIPTION

GenAPI is a program for gene presence absence table generation for series of closely related bacterial genomes from annotated GFF files.

::DEVELOPER

Migle Gabrielaite

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

GenAPI

:: MORE INFORMATION

Citation

Gabrielaite M, Marvig RL.
GenAPI: a tool for gene absence-presence identification in fragmented bacterial genome sequences.
BMC Bioinformatics. 2020 Jul 20;21(1):320. doi: 10.1186/s12859-020-03657-5. PMID: 32690023; PMCID: PMC7372895.

GREVE – Identification of Patterns across Individual Cancer Samples

GREVE

:: DESCRIPTION

GREVE (Genomic Recurrent Event ViEwer) has been developed to assist with the identification of recurrent genomic aberrations across cancer samples.

::DEVELOPER

Jean-Baptiste Cazier

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 GREVE

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Nov 15;28(22):2981-2. doi: 10.1093/bioinformatics/bts547. Epub 2012 Sep 8.
GREVE: Genomic Recurrent Event ViEwer to assist the identification of patterns across individual cancer samples.
Cazier JB1, Holmes CC, Broxholme J

SPPIDER – Solvent accessibility based Protein-Protein Interface iDEntification and Recognition

SPPIDER

:: DESCRIPTION

The SPPIDER protein interface recognition server can be used to: (1) predict residues to be at the putative protein interface(s) by considering single protein chain with resolved 3D structure; (2) analyse protein-protein complex with given 3D structural information and identify residues that are being in interchain contact.

::DEVELOPER

Meller Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Proteins. 2007 Feb 15;66(3):630-45.
Prediction-based fingerprints of protein-protein interactions.
Porollo A1, Meller J.

MINNOU – Membrane Protein IdeNtificatioN withOUt explicit use of Hydropathy Profiles and Alignments

MINNOU

:: DESCRIPTION

The MINNOU server can be used for predicting trans-membrane domains.

:: SCREENSHOTS

N/A

::DEVELOPER

Meller Lab

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MINNOU

:: MORE INFORMATION

Citation

Bioinformatics. 2006 Feb 1;22(3):303-9. Epub 2005 Nov 17.
Enhanced recognition of protein transmembrane domains with prediction-based structural profiles.
Cao B1, Porollo A, Adamczak R, Jarrell M, Meller J.

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