DIAproteomics 1.2.4 – Automated Quantitative Analysis of DIA Proteomics Mass Spectrometry Measurements

DIAproteomics 1.2.4

:: DESCRIPTION

diaproteomics is a bioinformatics analysis pipeline used for quantitative processing of data independant (DIA) proteomics data.

::DEVELOPER

the Science for Life Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Docker

:: DOWNLOAD

DIAproteomics

:: MORE INFORMATION

Citation

Bichmann L, Gupta S, Rosenberger G, Kuchenbecker L, Sachsenberg T, Ewels P, Alka O, Pfeuffer J, Kohlbacher O, Röst H.
DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics.
J Proteome Res. 2021 Jul 2;20(7):3758-3766. doi: 10.1021/acs.jproteome.1c00123. Epub 2021 Jun 21. PMID: 34153189.

OAT 0.91 – Similarity Measurement Tool for Genomes

OAT 0.91

:: DESCRIPTION

OAT (Orthologous Average Nucleotide Identity Tool) uses OrthoANI to measure overall similarity between two genome sequences.

::DEVELOPER

OAT team

:: SCREENSHOTS

OAT

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Java

:: DOWNLOAD

 OAT

:: MORE INFORMATION

Citation:

OrthoANI: An improved algorithm and software for calculating average nucleotide identity.
Lee I, Kim YO, Park SC, Chun J.
Int J Syst Evol Microbiol. 2015 Nov 18. doi: 10.1099/ijsem.0.000760.

MetProc 1.0 – Separate Metabolites into Likely Measurement Artifacts and True Metabolites

MetProc 1.0

:: DESCRIPTION

The goal of MetProc is to provide a simple and convenient set of metrics to help distinguish true metabolites from likely measurement artifacts by assessing patterns of missing data across an injection order.

::DEVELOPER

Liming Liang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  Windows
  • R

:: DOWNLOAD

MetProc

:: MORE INFORMATION

Citation

Chaffin MD, et al.
MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment.
J Proteome Res. 2019 Mar 1;18(3):1446-1450. doi: 10.1021/acs.jproteome.8b00893. Epub 2018 Dec 31. PMID: 30562035.

GS-align – Glycan Structure Alignment and Similarity Measurement

GS-align

:: DESCRIPTION

GS-align is a novel computational method for glycan structure alignment and similarity measurement. GS-align generates possible alignments between two glycan structures through iterative maximum clique search and fragment superposition, and the optimal alignment is determined by the maximum structural similarity score, GS-score whose significance is size-independent.

::DEVELOPER

Wonpil Im Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 GS-align

:: MORE INFORMATION

Citation

GS-align for Glycan Structure Alignment and Similarity Measurement.
Lee HS, Jo S, Mukherjee S, Park SJ, Skolnick J, Lee J, Im W.
Bioinformatics. 2015 Apr 8. pii: btv202

targetedRetrieval 120705 – Targeted Retrieval of Gene Expression Measurements Using Regulatory Models

targetedRetrieval 120705

:: DESCRIPTION

targetedRetrieval is a model for the regulation of specific genes from a data repository and exploit it to construct a similarity metric for an information retrieval task.

::DEVELOPER

Probabilistic Machine Learning

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /MacOsX
  • R package

:: DOWNLOAD

  targetedRetrieval

:: MORE INFORMATION

Citation

Elisabeth Georgii, Jarkko Salojärvi, Mikael Brosché, Jaakko Kangasjärvi, and Samuel Kaski.
Targeted retrieval of gene expression measurements using regulatory models.
Bioinformatics (2012) 28 (18): 2349-2356.

GPODE 20090927 – Learning Gene Regulatory Networks from Gene Expression Measurements

GPODE 20090927

:: DESCRIPTION

GPODE (GP4GRN) is a software of learning the structure of gene regulatory networks using non-parametric molecular kinetics. A set of Matlab functions that implement our gene regulatory network inference method. The method can use time-series and steady state gene expression (and protein) measurements and makes Bayesian inference for the network structure using Gaussian process based non-parametric molecular kinetics

::DEVELOPER

Tarmo Äijö

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • Matlab

:: DOWNLOAD

 GPODE

:: MORE INFORMATION

Citation:

Bioinformatics. 2009 Nov 15;25(22):2937-44. Epub 2009 Aug 25.
Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics.
Aijö T, Lähdesmäki H.

DSection – Probabilistic Analysis of Gene Expression Measurements from Heterogeneous Tissues

DSection

:: DESCRIPTION

DSection is a model for reconstructing cell type specific gene expression profiles from measurements of heterogeneous tissues.

DSection Online Version

::DEVELOPER

Timo Erkkilä

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • Matlab

:: DOWNLOAD

 DSection

:: MORE INFORMATION

Citation:

Probabilistic analysis of gene expression measurements from heterogeneous tissues.
Erkkilä T, Lehmusvaara S, Ruusuvuori P, Visakorpi T, Shmulevich I,Harri Lähdesmäki.
Bioinformatics. 2010 Oct 15;26(20):2571-7. Epub 2010 Jul 14.

ws2m 3.2 – Software for the Measurement and Analysis of Species Diversity

ws2m 3.2

:: DESCRIPTION

Ws2m does the job of estimating the total number of types in a finite collection. It was developed to estimate the number of species in a collection of identified individuals.

::DEVELOPER

Will Turner (wturner@u.arizona.edu), Wade Leitner, and Michael Rosenzweig

:: SCREENSHOTS

ws2m

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 Ws2m

:: MORE INFORMATION