CAPER 2.0: An Interactive, Configurable, and Extensible Workflow-Based Platform to Analyze Data Sets from the Chromosome-centric Human Proteome Project

Abstract

The Chromosome-centric Human Proteome Project (C-HPP) aims to map and annotate the entire human proteome by the "chromosome-by-chromosome" strategy. As the C-HPP proceeds, the increasing volume of proteomic data sets presents a challenge for customized and reproducible bioinformatics data analyses for mining biological knowledge. To address this challenge, we updated the previous static proteome browser CAPER into a higher version, CAPER 2.0 - an interactive, configurable and extensible workflow-based platform for C-HPP data analyses. In addition to the previous visualization functions of track-view and heatmap-view, CAPER 2.0 presents a powerful toolbox for C-HPP data analyses and also integrates a configurable workflow system that supports the view, construction, edit, run, and share of workflows. These features allow users to easily conduct their own C-HPP proteomic data analyses and visualization by CAPER 2.0. We illustrate the usage of CAPER 2.0 with four specific workflows for finding missing proteins, mapping peptides to chromosomes for genome annotation, integrating peptides with transcription factor binding sites from ENCODE data sets, and functionally annotating proteins. The updated CAPER is available at http://www.bprc.ac.cn/CAPE.

Flexible and Accessible Workflows for Improved Proteogenomic Analysis Using the Galaxy Framework

“…This output is also compatible with the recently described, Galaxy-based CAPER software for peptide to genome mapping. 24 …”

“…A number of laboratories have described algorithms and software for accomplishing at least a portion of these procedures to make up a proteogenomics workflow. 7 , 16 , 20 − 24 For example, various algorithms have been used for generating protein sequence databases from genomic/transcriptomic data. These include six-frame translation from genomic DNA sequence data 3 , 6 , 25 , 26 and three-frame translation from transcriptomic sequence data derived from cDNA 27 , 28 or RNA-Seq data.…”

“…A number of laboratories have described algorithms and software for accomplishing at least a portion of these procedures to make up a proteogenomics workflow. ,,− For example, various algorithms have been used for generating protein sequence databases from genomic/transcriptomic data. These include six-frame translation from genomic DNA sequence data ,,, and three-frame translation from transcriptomic sequence data derived from cDNA , or RNA-Seq data. ,,− Because the generated protein sequence databases are large, methods have been developed to reduce the database size and improve results when matching tandem mass spectrometry (MS/MS) to these sequences. ,, A number of different programs have been used for matching of MS/MS data to these databases, including both publicly available programs such as X!…”

“…Tandem, ,, Myrimatch, ,, and MS-GF+ 3 and commercial programs such as Mascot, Sequest, and ProteinPilot . After identification of possible novel peptide sequences, a number of steps for filtering and assessing confidence have been used, including BLAST analysis to verify putatively novel peptide spectral matches (PSMs). , Lastly, there are multiple tools to visualize peptide positions on the genome. ,, …”

PepPSy: a web server to prioritize gene products in experimental and biocuration workflows

“…Customizable workflows, such as CAPER 2.0 ( 10 ), have already been used to discover novel genes by integrating transcriptomic and proteomic data ( 11 ). In the HPP context, the use of transcriptomics datasets has also proven to be valuable to determine where to look for missing proteins ( 12–14 ).…”