ICSNPathway 1.1 – Identify Candidate Causal SNPs and Pathways from Genome-wide Association Study

ICSNPathway 1.1

:: DESCRIPTION

ICSNPathway is a web server developed to discover candidate causal SNPs and corresponding candidate causal pathways from genome-wide association study (GWAS).

::DEVELOPER

Bioinformatics Lab, Institute of Psychology, Chinese Academy of Sciences

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / MacOsX
  • Java

:: DOWNLOAD

  ICSNPathway

:: MORE INFORMATION

Citation

K. Zhang, S. Chang, et al. (2011).
ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework.”
Nucleic Acids Res. 39(suppl 2): W437-W443.

VAAST 2.0 – Identify Damaged Genes and Disease-causing Variants in Personal Genome Sequences

VAAST 2.0

:: DESCRIPTION

VAAST (the Variant Annotation, Analysis and Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds upon existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood-framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and non-coding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology.

::DEVELOPER

Yandell Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 VAAST

:: MORE INFORMATION

Citation:

VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix
Hu H Huff CD Moore B Flygare S Reese MG Yandell M
Genet Epidemiol. 2013 37(6):622-34.

A probabilistic disease-gene finder for personal genomes
Yandell M Huff CD Hu H Singleton M Moore B Xing J Jorde L Reese MG
Genome Res. 2011 doi:10.1101/gr.123158.111

DISTILLER 2.0 – Data Integration System To Identify Links in Expression Regulation

DISTILLER 2.0

:: DESCRIPTION

DISTILLER (Data Integration System To Identify Links in Expression Regulation) is a data integration framework that searches for transcriptional modules by combining expression data with information on the direct interaction between a regulator and its corresponding target genes. The framework builds upon advanced itemset mining approaches that have been designed to have good scalability, efficient memory use, and a small number of user parameters. It includes a condition selection or bicluster strategy in which co-expression of genes is required in only a significant subset of the complete condition set. By including this condition selection we can apply the algorithm to large expression compendia where interesting genes are not necessarily co-expressed in all measured conditions. Our approach also makes it straightforward to include any number of data sources related to transcriptional interactions such as additional microarrays, ChIP-chip or motif data.

::DEVELOPER

Kathleen Marchal 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows / MacOsX
  • Java
:: DOWNLOAD

 DISTILLER

:: MORE INFORMATION

Citation

Lemmens K, De Bie T, Dhollander T, De Keersmaecker SC, Thijs IM, Schoofs G, De Weerdt A, De Moor B, Vanderleyden J, Collado-Vides J, Engelen K, Marchal K.
DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli“.
Genome Biology , 10:R27 doi:10.1186/gb-2009-10-3-r27 (2009).

MCScanX / MCScanX-transposed – Scan multiple Genomes to Identify Homologous Chromosomal Regions and more

MCScanX/ MCScanX-transposed

:: DESCRIPTION

MCScanX toolkit implements an adjusted MCScan algorithm for detection of synteny and collinearity and extends the software by incorporating 15 utility programs for display and further analyses.

MCScanX-transposed: detecting transposed gene duplications based on multiple collinearity scans

::DEVELOPER

Haibao Tang : bao at uga dot edu and  Yupeng Wang: wyp1125@gmail.com at Plant Genome Mapping Laboratory, University of Georgia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /MacOsX
  • Java

:: DOWNLOAD

MCScanX / MCScanX-transposed

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jun 1;29(11):1458-60. doi: 10.1093/bioinformatics/btt150. Epub 2013 Mar 28.
MCScanX-transposed: detecting transposed gene duplications based on multiple colinearity scans.
Wang Y1, Li J, Paterson AH.

Nucleic Acids Res. 2012 Apr;40(7):e49. doi: 10.1093/nar/gkr1293. Epub 2012 Jan 4.
MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity.
Wang Y, Tang H, Debarry JD, Tan X, Li J, Wang X, Lee TH, Jin H, Marler B, Guo H, Kissinger JC, Paterson AH.

Socrates 0.95 – SOft Clip re-alignment To IdEntify Structural Variants

Socrates 0.95

:: DESCRIPTION

Socrates is a highly efficient and effective method for detecting genomic rearrangements in tumours that utilises split-read data. Socrates features single nucleotide resolution, high sensitivity, and high specificity in simulated data.

::DEVELOPER

WEHI Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Socrates

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jan 22. [Epub ahead of print]
Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads.
Schröder J1, Hsu A, Boyle SE, Macintyre G, Cmero M, Tothill RW, Johnstone RW, Shackleton M, Papenfuss AT.

pdCSM-PPI – Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors

pdCSM-PPI

:: DESCRIPTION

pdCSM-PPI is a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Rodrigues CHM, Pires DEV, Ascher DB.
pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors.
J Chem Inf Model. 2021 Nov 22;61(11):5438-5445. doi: 10.1021/acs.jcim.1c01135. Epub 2021 Nov 1. PMID: 34719929.

V-Phaser 2.0 / V-Profiler 1.0 – Variant Calling and Visualization tools to Identify Biological Mutations in Diverse populations

V-Phaser 2.0 / V-Profiler 1.0

:: DESCRIPTION

V-Phaser is a tool to call variants in genetically heterogeneous populations from ultra-deep sequence data. V-Phaser combines information regarding the covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity.

V-Profiler takes a read alignment and a list of accepted variants at each location in the alignment (such as would be generated by V-Phaser) and analyzes the intra-host diversity of a genome. This can be done at the nucleotide level over the whole sequence, at the codon level for each gene specified in a list, and at the haplotype level for any region delimited (note that the region must not exceed a read length, and is preferably of shorter length such as an epitope or a loop of interest).

::DEVELOPER

Computational R&D, The Broad Institute, Cambridge, MA

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 V-Phaser  / V-Profiler

:: MORE INFORMATION

Citation

BMC Genomics. 2013 Oct 3;14:674. doi: 10.1186/1471-2164-14-674.
V-Phaser 2: variant inference for viral populations.
Yang X, Charlebois P, Macalalad A, Henn MR, Zody MC.

PLoS Comput Biol. 2012;8(3):e1002417. doi: 10.1371/journal.pcbi.1002417. Epub 2012 Mar 15.
Highly sensitive and specific detection of rare variants in mixed viral populations from massively parallel sequence data.
Macalalad AR, Zody MC, Charlebois P, Lennon NJ, Newman RM, Malboeuf CM, Ryan EM, Boutwell CL, Power KA, Brackney DE, Pesko KN, Levin JZ, Ebel GD, Allen TM, Birren BW, Henn MR.

genoCN 1.09 – Identify Copy Number States and Genotype Calls.

genoCN 1.09

:: DESCRIPTION

GenoCN is a software that simultaneously identify copy number states and genotype calls. Different strategies are implemented for the study of Copy Number Variations (CNVs) and Copy Number Aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared to CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. genoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue.

::DEVELOPER

Wei Sun

:: SCREENSHOTS

N/A

::REQUIREMENTS

:: DOWNLOAD

  genoCN

:: MORE INFORMATION

Citation

Sun, W., Wright , F., Tang, Z.Z., Nordgard , S.H., Van Loo, P., Yu, T., Kristensen, V., Perou, C.,
Integrated study of copy number states and genotype calls using high density SNP arrays.
Nucleic Acids Res. 2009, 37(16), 5365-77

PClouds 1.0 – Identify Repeat Structure in large Eukaryotic Genomes

PClouds 1.0

:: DESCRIPTION

The (PClouds) P-clouds package is designed to identify repeat structure in large eukaryotic genomes using oligonucleotide counts.

::DEVELOPER

Pollock Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • C++ Compiler

:: DOWNLOAD

  PClouds

:: MORE INFORMATION

Citation

W. Gu, T. A. Castoe, D. J. Hedges, M. A. Batzer, and D. D. Pollock,
Identification of repeat structure in large genomes using repeat probability clouds .”
Anal Biochem. 2008 Sep 1;380(1):77-83. Epub 2008 May 20.

Meerkat 0.189 – Identify Structural Variations

Meerkat 0.189

:: DESCRIPTION

Meerkat is designed to identify structure variations (SVs) from paired end high throughput sequencing data. It predicts SVs from discordant read pairs (pairs that mapped to reference genome in unexpected way).

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Meerkat

:: MORE INFORMATION

Citation

Cell. 2013 May 9;153(4):919-29. doi: 10.1016/j.cell.2013.04.010.
Diverse mechanisms of somatic structural variations in human cancer genomes.
Yang L, Luquette LJ, Gehlenborg N, Xi R, Haseley PS, Hsieh CH, Zhang C, Ren X, Protopopov A, Chin L, Kucherlapati R, Lee C, Park PJ.