scHiCluster v0.1.1 – Single-cell Clustering algorithm for Hi-C Contact Matrices

scHiCluster v0.1.1

:: DESCRIPTION

scHiCluster is a comprehensive python package for single-cell chromosome contact data analysis. It includes the identification of cell types (clusters), loop calling in cell types, and domain and compartment calling in single cells.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

scHiCluster

:: MORE INFORMATION

Citation:

Zhou J, Ma J, Chen Y, Cheng C, Bao B, Peng J, Sejnowski TJ, Dixon JR, Ecker JR.
Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation.
Proc Natl Acad Sci U S A. 2019 Jul 9;116(28):14011-14018. doi: 10.1073/pnas.1901423116. Epub 2019 Jun 24. PMID: 31235599; PMCID: PMC6628819.

DCell 1.4 – Deep Neural Network simulating Cell Structure and Function

DCell 1.4

:: DESCRIPTION

DCell is a neural network model of budding yeast, a basic eukaryotic cell. The model structure corresponds exactly to a hierarchy of 2,526 cellular subsystems.

::DEVELOPER

Ma Laboratory / Ideker Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

DCell

:: MORE INFORMATION

Citation:

Ma J, Yu MK, Fong S, Ono K, Sage E, Demchak B, Sharan R, Ideker T.
Using deep learning to model the hierarchical structure and function of a cell.
Nat Methods. 2018 Apr;15(4):290-298. doi: 10.1038/nmeth.4627. Epub 2018 Mar 5. PMID: 29505029; PMCID: PMC5882547.

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.

ImmuCellAI / ImmuCellAI-mouse – Immune Cell Abundance Identifier / for mouse

ImmuCellAI / ImmuCellAI-mouse

:: DESCRIPTION

ImmuCellAI is a tool to estimate the abundance of 24 immune cells from gene expression dataset including RNA-Seq and microarray data, in which the 24 immune cells are comprised of 18 T-cell subtypes and 6 other immune cells: B cell, NK cell, Monocyte cell, Macrophage cell, Neutrophil cell and DC cell.

ImmuCellAI-mouse is the mouse version of ImmuCellAI. ImmuCellAI-mouse is a tool to estimate the abundance of 36 immune cells based on gene expression profile from RNA-Seq or microarray data. To ensure the prediction accuracy, we adopted a hierarchical strategy to classify the 36 cell types into three layers, which were presented as inclusion relation among different circles in the right figure.

::DEVELOPER

An-Yuan Guo’s Bioinformatics Laboratory

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web Browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Miao YR, Xia M, Luo M, Luo T, Yang M, Guo AY.
ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction.
Bioinformatics. 2021 Oct 12:btab711. doi: 10.1093/bioinformatics/btab711. Epub ahead of print. PMID: 34636837.

Miao YR, Zhang Q, Lei Q, Luo M, Xie GY, Wang H, Guo AY.
ImmuCellAI: A Unique Method for Comprehensive T-Cell Subsets Abundance Prediction and its Application in Cancer Immunotherapy.
Adv Sci (Weinh). 2020 Feb 11;7(7):1902880. doi: 10.1002/advs.201902880. PMID: 32274301; PMCID: PMC7141005.

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.

flowLearn – Machine-learning algorithm for Gating Flow Cytometry data

flowLearn

:: DESCRIPTION

flowLearn is a semi-supervised approach for the quality-checked identification of cell populations.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

flowLearn

:: MORE INFORMATION

Citation

Lux M, Brinkman RR, Chauve C, Laing A, Lorenc A, Abeler-Dörner L, Hammer B.
flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry.
Bioinformatics. 2018 Jul 1;34(13):2245-2253. doi: 10.1093/bioinformatics/bty082. PMID: 29462241; PMCID: PMC6022609.

SIMBA v1.1 – SIngle-cell eMBedding Along with features

SIMBA v1.1

:: DESCRIPTION

SIMBA is a method to embed cells along with their defining features such as gene expression, transcription factor binding sequences and chromatin accessibility peaks into the same latent space.

::DEVELOPER

Pinello Lab.

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

SIMBA

:: MORE INFORMATION

Citation

Preprint: Huidong Chen, Jayoung Ryu, Michael E. Vinyard, Adam Lerer & Luca Pinello.
“SIMBA: SIngle-cell eMBedding Along with features. bioRxiv, 2021.10.17.464750v1 (2021).”

BioLegend Flow Cytometry Application 1.6 – Information about Flow Cytometers and Fluorochromes

BioLegend Flow Cytometry Application 1.6

:: DESCRIPTION

BioLegend Flow Cytometry Application provides you with important information about your Flow Cytometers and Fluorochromes. Now you can use BioLegend’s Spectra Analyzer quickly and conveniently in the palm of your hand. We included an Antibody Usage Calculator and Timer to help with your experiments (Timer does not work in the background and will pause upon exiting the application).

::DEVELOPER

BioLegend

:: SCREENSHOTS

:: REQUIREMENTS

  • iPhone /  iPad

:: DOWNLOAD

 BioLegend Flow Cytometry Application

:: MORE INFORMATION

CBO 1.1.2 – Describing the Intrinsic Biological Behaviors of Real and Model Cells

CBO 1.1.2

:: DESCRIPTION

The CBO (Cell Behavior Ontology) is designed to describe multi-cell computational models. In particular to describe both the existential behaviors of cells (spatiality, growth, movement, adhesion, death, …) and computational models of those behaviors.The CBO is an OWL-2 ontology developed in Protégé 4

::DEVELOPER

Biocomplexity Institute, Indiana University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 CBO

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Apr 22. pii: btu210.
The cell behavior ontology: describing the intrinsic biological behaviors of real and model cells seen as active agents.
Sluka JP, Shirinifard A, Swat M, Cosmanescu A, Heiland RW, Glazier JA.

SPICE 6.1001 – Data Mining & Visualization Software for Multicolor Flow Cytometry

SPICE 6.1001

:: DESCRIPTION

SPICE is a data mining software application that analyzes large FLOWJO data sets from polychromatic flow cytometry and organizes the normalized data graphically. SPICE enables users to discover potential correlations in their experimental data within complex data sets.

::DEVELOPER

Bioinformatics and Computational Biosciences Branch (BCBB),National Institute of Allergy and Infectious Diseases (NIAID)

:: SCREENSHOTS

:: REQUIREMENTS

  • Mac OsX

:: DOWNLOAD

 SPICE

:: MORE INFORMATION

Citation

Cytometry A. 2011 Feb;79(2):167-74. doi: 10.1002/cyto.a.21015. Epub 2011 Jan 7.
SPICE: exploration and analysis of post-cytometric complex multivariate datasets.
Roederer M, Nozzi JL, Nason MC.

Exit mobile version