HDMC – Hierarchical Distribution Matching and Contrastive learning

HDMC

:: DESCRIPTION

HDMC is a novel deep learning based framework for batch effect removal in scRNA-seq data.

::DEVELOPER

HDMC team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

HDMC

:: MORE INFORMATION

Citation:

Wang X, Wang J, Zhang H, Huang S, Yin Y.
HDMC: a novel deep learning based framework for removing batch effects in single-cell RNA-seq data.
Bioinformatics. 2021 Dec 4:btab821. doi: 10.1093/bioinformatics/btab821. Epub ahead of print. PMID: 34864918.

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

TRAP 3.05 – Transcription factor Affinity Prediction

TRAP 3.05

:: DESCRIPTION

TRAP (Transcription factor Affinity Prediction) calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation–sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism.

::DEVELOPER

TRAP Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ compiler
  • R Package

:: DOWNLOAD

  TRAP

:: MORE INFORMATION

Citation

Morgane Thomas-Chollier, Andrew Hufton, Matthias Heinig, Sean O’Keeffe, Nassim El Masri, Helge G Roider, Thomas Manke and Martin Vingron.
Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs.
Nature Protocols, 3;6(12):1860-9. (2011)

SLIP 1.0.1 / SLIDE 1.0.4 – Rapid Multiple Hypothesis Testing Correction / Power Estimation

SLIP 1.0.1 / SLIDE 1.0.4

:: DESCRIPTION

SLIP (Sliding-window method for Locally Inter-correlated markers for Power estimation) is a multivariate normal distribution (MVN)-based power estimation method. SLIP shows a near identical accuracy to the standard simulation procedure for power, and is much faster.

SLIP (Sliding-window method for Locally Inter-correlated markers for Power estimation) is a multivariate normal distribution (MVN)-based power estimation method. SLIP shows a near identical accuracy to the standard simulation procedure for power, and is much faster.

::DEVELOPER

ZarLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Mac OsX

:: DOWNLOAD

 SLIP / SLIDE 

:: MORE INFORMATION

Citation

Buhm Han, Hyun Min Kang, and Eleazar Eskin (2009)
Rapid and accurate multiple testing correction and power estimation for millions of correlated markers.
PLoS Genet 5(4): e1000456. doi:10.1371/journal.pgen.1000456

OnScreen DNA 2.6 – Virtual DNA Model

OnScreen DNA 2.6

:: DESCRIPTION

OnScreen DNA is an easy-to-use, interactive virtual DNA model you can use to show these key details—and many more—in a memorable way that develops understanding. The program has been designed, first of all, to allow students to become completely at home with the essential details of DNA’s three-dimensional double helix structure.

::DEVELOPER

OnScreen Science, Inc

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOSX / Ipad / Iphone

:: DOWNLOAD

OnScreen DNA

:: MORE INFORMATION

SPOCS 1.0.10 – Graph-based Ortholog/Paralog Prediction tool

SPOCS 1.0.10

:: DESCRIPTION

SPOCS (Species Paralogy and Orthology Clique Solver) is a graph-based ortholog/paralog prediction tool that will predict orthologs and paralogs given a set of prokaryotic proteomes (the set of proteins encoded by a genome). The software will take a set of protein fasta files (one per species genome), and an optional additional fasta to serve as an outgroup (a species that should be more distantly related to the species of interest than any of the species of interest are to each other).

::DEVELOPER

Computational Biology & Bioinformatics ,Pacific Northwest National Laboratory

:: SCREENSHOTS

SPOCS

:: REQUIREMENTS

  • Linux/ MacOsX
  • C++ Compiler
  • NCBI BLAST

:: DOWNLOAD

 SPOCS

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Oct 15;29(20):2641-2. doi: 10.1093/bioinformatics/btt454.
SPOCS: software for predicting and visualizing orthology/paralogy relationships among genomes.
Curtis DS, Phillips AR, Callister SJ, Conlan S, McCue LA.

VISCOE 1.0 – Visual Integration Software for Conditional Omics Experiments

VISCOE 1.0

:: DESCRIPTION

VISCOE is a genomic integration tool to simultaneously visualize proteomics experiments across multiple conditions. VISCOE maps peptides onto proteins on the appropriate DNA strand and offers peptide- and protein-centric searches to find regions of qualitative differences (up to six conditions). VISCOE is also capable of integration of RNA-seq transcriptomic data for simultaneous visualization. RNA-seq data can be represented in SAM or WIG format.

::DEVELOPER

Computational Biology & Bioinformatics ,Pacific Northwest National Laboratory

:: SCREENSHOTS

VISCOE

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java

:: DOWNLOAD

VISCOE

:: MORE INFORMATION

Proteios 2.19.0 – Multi-user Platform for Analysis and Management of Proteomics data

Proteios 2.19.0

:: DESCRIPTION

Proteios SE (or ProSE) is meant to be installed on a local server in a proteomics laboratory.

::DEVELOPER

Jari Häkkinen @ Lund University

:: SCREENSHOTS

Proteios

:: REQUIREMENTS

:: DOWNLOAD

  Proteios

:: MORE INFORMATION

Citation

J Proteome Res. 2009 Jun;8(6):3037-43. doi: 10.1021/pr900189c.
The proteios software environment: an extensible multiuser platform for management and analysis of proteomics data.
Häkkinen J, Vincic G, Månsson O, Wårell K, Levander F.

Baescs 2.0 – Estimate Calibration Curves and unknown Concentrations in Immunoassays

Baescs 2.0

:: DESCRIPTION

Baescs is a Bayesian approach to estimate calibration curves and unknown concentrations in immunoassays.

::DEVELOPER

The Center for Computational Immunology 

:: SCREENSHOTS

Baescs

::REQUIREMENTS

  • Linux/Windows

:: DOWNLOAD

 Baescs

:: MORE INFORMATION

Citation

Feng Feng; Ana Paula Sales; Thomas B. Kepler :
A Bayesian approach for estimating calibration curves and unknown concentrations in immunoassays.
Bioinformatics (2011) 27 (5): 707-712.

sppPCA 1.0 – Sequential Projection Pursuit PCA

sppPCA 1.0

:: DESCRIPTION

The sppPCA method presented here provides an approach for researchers to perform exploratory data analysis on new -omic datasets containing missing data. By removing the necessity to impute missing values, the results of the low-dimensional projections of the data are not skewed by inaccurate estimates of variance, which is often introduced by imputation. Sequential projection pursuit (SPP) is a computationally robust approach for performing the optimization task to identify the small subset of orthogonal latent variables of interest (e.g., principal components).

:: DEVELOPER

Computational Biology & Bioinformatics ,Pacific Northwest National Laboratory

:: SCREENSHOTS

sppPCA

:: REQUIREMENTS

  • Windows
  • Matlab
  • JAVA

:: DOWNLOAD

 sppPCA

:: MORE INFORMATION

Citation

Biotechniques. 2013 Mar;54(3):165-8. doi: 10.2144/000113978.
Sequential projection pursuit principal component analysis–dealing with missing data associated with new -omics technologies.
Webb-Robertson BJ, Matzke MM, Metz TO, McDermott JE, Walker H, Rodland KD, Pounds JG, Waters KM.