Software

1. Color Selection Tools for Quantifying Histological Data

Description

Current methods for selecting target color of interest often utilize thresholds in the RGB color space. However, this can be imprecise and prone to wide variation. We have found that color separation using the HSV color space is highly consistent with visual recognition of color and much more reliable that the conventional RGB color selection method. We have published the results (*1) and have provided the software tools we reference.

A Hue Saturation Value Method for Quantifying Histological Data. Katsumi Yabusaki, Tyler Faits, Eri McMullen, Jose-Luiz Figueiredo, Masanori Aikawa and Elena Aikawa., Plos One (2014).

Software tools are available online.

2. XINA (multiplexed isobaric mass tagged-based kinetics data for network analysis)

Description

XINA is the updated software of mIMT-visHTS [J Proteomics, 2015; 125:132-14, PMID: 26232111]. XINA combines multiple quantitative (kinetics) datasets from omics studies into a single input dataset for clustering. XINA, not only extracts co-abundance profiles within and across experimental datasets, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively.While users still require basic knowledge of R programming, XINA is designed for non-expert users aiming to perform network analyses on their proteomics data.

The downloadable package includes:

  1. XINA manual
  2. XINA user code
  3. R codes for XINA install 
  4. XINA package (XINA_1.0.0.tar.gz)

To use XINA, we recommend you to install the following two:

  1. R version (>3.5)
  2. R-studio ( https://www.rstudio.com/)

XINA’s Github repository link ( https://github.com/langholee/XINA )

To install up-to-date version of XINA from Github:

  1. install.packages('devtools')
  2. library('devtools')
  3. install_github('langholee/XINA')

XINA imports these R packages: mclust, stringr, plyr, circlize, alluvial, ggplot2, RColorBrewer, graphics, gridExtra, igraph, STRINGdb, Biobase

3. XPI (The extracted PRM peak intensity)

Description

The XPI program was developed to quantify parallel reaction monitoring (PRM) data of stable isotope labeled peptides. As a result, this software is currently optimized for Thermo instrument .RAW file data. The XPI program extracts the centroided peak intensity of each PRM target ion scan.

Automation of PRM-dependent D3-Leu tracer enrichment in HDL to study the metabolism of apoA-I, LCAT and other apolipoproteins. Lee LH, Andraski AB, Pieper B, Higashi H, Sacks FM, Aikawa M, Singh SA. Proteomics. 2017 Jan;17(1-2).

Please contact sasingh@bwh.havard.edu if you have any comments or questions

The downloadable package includes:

  1. Manual_v1.0.pdf: The user guide for XPI
  2. Testset: it has mzML files, an inclusion list and configuration files for an user’s quick test.(for Mac or for Windows)
  3. XPILib.pyc: XPI library compiled code (for Mac or for Windows)
  4. XPIPeak.py: XPI peak detection script (for Mac or for Windows)
  5. XPIQuant.py: XPI quantification script (for Mac or for Windows)
  6. XPIViz.py: XPI visualization script (for Mac or for Windows)

Download full package for Mac or for Windows.


4. Flexible pattern Particle Labeling (FpPL)

Description

This software identifies small spherical particles in noisy images. The software was originally designed to identify small calcified particles in electron micrographs of cardiovascular tissue.

Quantification of Calcified Particles in Human Valve Tissue Reveals Asymmetry of Calcific Aortic Valve Disease Development. Yabusaki K, Hutcheson JD, Vyas P, Bertazzo S, Body SC, Aikawa M, Aikawa E. Frontiers in Cardiovascular Medicine. 2016 Nov 4;3:44. eCollection 2016.

Please contact eaikawa@bwh.havard.edu if you have any comments or questions.

The downloadable package includes:

  1. A brief tutorial in the ReadMe file
  2. The FpPl.exe file
  3. Example .png input and output files
  4. A configuration file