Tag: Cancer

nFuse 0.2.1 – Discovery of Complex Genomic Rearrangements in Cancer

nFuse 0.2.1

:: DESCRIPTION

nFuse is a tool for detecting fusion transcripts and associated complex genomic rearrangements from matched RNA-seq and whole genome shotgun sequencing.nFuse predicts fusion transcripts and associated CGRs from matched RNA-seq and Whole Genome Shotgun Sequencing (WGSS).

::DEVELOPER

Andrew McPherson (andrew.mcpherson@gmail.com)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 nFuse

:: MORE INFORMATION

Citation:

McPherson AW, Wu C, Wyatt A, Shah SP, Collins C, Sahinalp SC.
nFuse: Discovery of complex genomic rearrangements in cancer using high-throughput sequencing.
Genome Res. 2012 Jun 28.

CONTOUR v2 – Cancer (Onco) geNes from disrupTed mOdUles and their Relationships

CONTOUR v2

:: DESCRIPTION

CONTOUR is a systematic tracking of dysregulated modules identifies novel genes in cancer

::DEVELOPER

CONTOUR team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C/C++ Compiler

:: DOWNLOAD

 CONTOUR

:: MORE INFORMATION

Citation

Srihari S & Ragan MA (2013)
Systematic tracking of dysregulated modules identifies novel genes in cancer,
Bioinformatics 29(12):1553–1561. doi: 10.1093/bioinformatics/btt191

GAP 2009 – Mining complex Cancer Genomic Profiles

GAP 2009

:: DESCRIPTION

GAP (Genome Alteration Print) is a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays

::DEVELOPER

Tatiana Popova, Institut Curie, Paris

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Mac OsX
  • R package

:: DOWNLOAD

 GAP

:: MORE INFORMATION

Citation

Genome Alteration Print (GAP): a tool to visualize and mine complex cancer genomic profiles obtained by SNP arrays.
Popova T, Manié E, Stoppa-Lyonnet D, Rigaill G, Barillot E, Stern MH.
Genome Biol. 2009;10(11):R128. doi: 10.1186/gb-2009-10-11-r128.

CCLA – Cancer Cell Line Authentication

CCLA

:: DESCRIPTION

CCLA is a web server to authenticate human cancer cell lines (CCLs) using expression profiles from RNA-Seq or microarray data.

::DEVELOPER

An-Yuan Guo’s Bioinformatics Laboratory

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Zhang Q, Luo M, Liu CJ, Guo AY.
CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles.
Brief Bioinform. 2021 May 20;22(3):bbaa093. doi: 10.1093/bib/bbaa093. PMID: 32510568.

GSCA / GSCALite – Gene Set Cancer Analysis

GSCA / GSCALite

:: DESCRIPTION

GSCA is an integrated database for genomic and immunogenomic gene set cancer analysis.

GSCALite is a web-based analysis platform for gene set cancer analysis. The alterations on DNA or RNA of cancer related genes may be contribute to the cancer initiation, progress, diagnosis, prognosis, therapy. As the cancer genomics big data available, it is very useful and urgent to provide a platform for gene set analysis in cancer.

::DEVELOPER

An-Yuan Guo’s Bioinformatics Laboratory

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY.
GSCALite: a web server for gene set cancer analysis.
Bioinformatics. 2018 Nov 1;34(21):3771-3772. doi: 10.1093/bioinformatics/bty411. PMID: 29790900.

Tronco v2.26.0 – Inference of Cancer Progression Models

Tronco v2.26.0

:: DESCRIPTION

Tronco (TRONCO TRanslational ONCOlogy)is an R suite for state-of-the-art algorithms for the reconstruction of causal models of cancer progressions from genomic cross-sectional data.

::DEVELOPER

Data and Computational Biology @ University of Milan – Bicocca.

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux/windows/MacOsX
  • R
  • BioConductor
  • rgraphviz

:: DOWNLOAD

 Tronco

:: MORE INFORMATION

Citation

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data.
De Sano L, Caravagna G, Ramazzotti D, Graudenzi A, Mauri G, Mishra B, Antoniotti M.
Bioinformatics. 2016 Feb 9. pii: btw035.

CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data.
Ramazzotti D, Caravagna G, Olde-Loohuis L, Graudenzi A, Korsunsky I, Mauri G, Antoniotti M, Mishra B.
Bioinformatics. 2015 May 13. pii: btv296.

CELLector v1.2.1 – Genomics Guided Selection of Cancer in Vitro Models

CELLector v1.2.1

:: DESCRIPTION

CELLector is a computational tool for selecting the most clinically relevant cancer cell lines to be included in a new in-vitro study (or to be considered in a retrospective study), in a patient-genomic guided fashion.

::DEVELOPER

Cancer Dependency Map Analytics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

CELLector

:: MORE INFORMATION

Citation

Najgebauer H, Yang M, Francies HE, Pacini C, Stronach EA, Garnett MJ, Saez-Rodriguez J, Iorio F.
CELLector: Genomics-Guided Selection of Cancer In Vitro Models.
Cell Syst. 2020 May 20;10(5):424-432.e6. doi: 10.1016/j.cels.2020.04.007. PMID: 32437684.

CHASM 3.0 / CHASMplus 1.0 – Cancer-specific High-throughput Annotation of Somatic Mutations

CHASM 3.0 / CHASMplus 1.0

:: DESCRIPTION

CHASM is a method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage.

CHASMplus is a machine learning method that accurately distinguishes between driver and passenger missense mutations, even for those found at low frequencies or are cancer type-specific.

::DEVELOPER

Karchin Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • MySQL Server
  • Python module MySQLdb

:: DOWNLOAD

CHASMplus

:: MORE INFORMATION

Citation

Tokheim C, Karchin R.
CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers.
Cell Syst. 2019 Jul 24;9(1):9-23.e8. doi: 10.1016/j.cels.2019.05.005. Epub 2019 Jun 12. PMID: 31202631; PMCID: PMC6857794.

CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer.
Wong WC, Kim D, Carter H, Diekhans M, Ryan MC, Karchin R.
Bioinformatics. 2011 Aug 1;27(15):2147-8. doi: 10.1093/bioinformatics/btr357. Epub 2011 Jun 17.

TransFIC – Transformed Functional Impact score for Cancer

TransFIC

:: DESCRIPTION

TransFIC is a method to transform Functional Impact scores taking into account the differences in basal tolerance to germline SNVs of genes that belong to different functional classes. This transformation allows to use the scores provided by well-known tools (e.g. SIFT, Polyphen2, MutationAssessor) to rank the functional impact of cancer somatic mutations. Mutations with greater transFIC are more likely to be cancer drivers.

::DEVELOPER

 The Biomedical Genomics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Perl

:: DOWNLOAD

 TransFIC

:: MORE INFORMATION

Citation

Abel Gonzalez-Perez, Jordi Deu-Pons and Lopez-Bigas N.
Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation.
Genome Medicine. 2012. 4:89 doi:10.1186/gm390.

CGARS – Cancer Genome Analysis by Rank Sums

CGARS

:: DESCRIPTION

CGARS is designed to dissect random from non-random patterns in copy number data and thereby to assess significantly enriched somatic copy number aberrations across a set of tumor specimens or cell lines. In contrast to existing approaches, the method is invariant to any strictly monotonous transformation of the input data, which results to an insensitivity of differences in tumor purity, array saturation effects, and copy number baseline levels.

::DEVELOPER

CGARS team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX

:: DOWNLOAD

 CGARS

:: MORE INFORMATION

Citation:

Bioinformatics. 2014 May 1;30(9):1295-6. doi: 10.1093/bioinformatics/btu011. Epub 2014 Jan 9.
CGARS: cancer genome analysis by rank sums.
Lu X1, Thomas RK, Peifer M.

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