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High-Throughput
Special Issues
Algorithms and Tools in Computational Proteomics
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Algorithms and Tools in Computational Proteomics

A special issue of High-Throughput (ISSN 2571-5135).

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 5507

Special Issue Editor

Dr. Bobbie-Jo Webb-Robertson

E-Mail Website
Guest Editor
Pacific Northwest National Laboratory
Interests: bioinformatics; computational biology; statistics; machine learning; proteomics

Special Issue Information

Dear Colleagues,

Computational proteomics is focused on statistical methods, algorithms, databases and other computational approaches to process, analyze and interpret proteomic data. The high-throughput nature of proteomic data, as well as the specific nuances of the data introduced by measurement via mass spectrometry, generate unique challenges that require novel methods for data handling, data processing, statistical analyses, as well as modeling and interpretation of the results. This Special Issue focuses on state-of-the-art approaches to handling proteomics data from the initial steps of data generation through the final steps of visualization and interpretation, as well as the essential functions of quality control and statistics that happen in-between.

Dr. Bobbie-Jo Webb-Robertson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. High-Throughput is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data management
  • quality control
  • statistical analyses
  • machine learning
  • modeling and interpretation
  • visualization

Published Papers (1 paper)

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Research

30 pages, 5464 KiB  
Article
Dark Proteome Database: Studies on Dark Proteins
by Nelson Perdigão and Agostinho Rosa
High-Throughput 2019, 8(2), 8; https://doi.org/10.3390/ht8020008 - 27 Mar 2019
Cited by 12 | Viewed by 5184
Abstract
The dark proteome, as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016), [...] Read more.
The dark proteome, as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016), 43.2% of the eukarya universe and 49.2% of the virus universe are part of the dark proteome. In bacteria and archaea, the percentage of the dark proteome presence is significantly less, at 12.6% and 13.3% respectively. In this work, we present a necessary step to complete the dark proteome picture by introducing the map of the dark proteome in the human and in other model organisms of special importance to mankind. The most significant result is that around 40% to 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of ‘dark’ genes that are responsible for the production of so-called dark proteins, as well as the identification of the ‘dark’ tissues where dark proteins are over represented, namely, the heart, cervical mucosa, and natural killer cells. This is a step forward in the direction of gaining a deeper knowledge of the human dark proteome. Full article
(This article belongs to the Special Issue Algorithms and Tools in Computational Proteomics)
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